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TRANSCRIPT
Image Processing for Detection of
Cataract, Retinopathy Of
Prematurity and Glaucoma
Arezoo Motamed Ektesabi
Faculty of Science, Engineering and Technology
Swinburne University of Technology
A thesis submitted for the degree of
Doctor of Philosophy
2015
i
Declaration
This thesis is the result of my own work and to the best of my knowl-
edge, includes nothing, which is the outcome of work done in collabo-
ration except where specifically indicated in the text. It has not been
previously submitted, in part or whole, to any university of institution
for any degree, diploma, or other qualification.
Signature:
Arezoo Motamed Ektesabi
2015
I would like to dedicate this thesis to my loving parents,
Mehran Motamed Ektesabi & Sharareh Soufi Siavash
and my brother, Arman Motamed Ektesabi
In memories of Shirin Salimi Pirkouhi, my grandmother,
who was always inspiring me to continue my studies.
Acknowledgements
I would forever be grateful for all those who guided me and encouraged
me to challenge myself, never give up, advance and succeed in life.
A special thanks goes to my principal coordinating supervisor, Profes-
sor Ajay Kapoor, for his mentoring and support throughout my candi-
dature. It was with his continuous guidance, commentary, suggestions
and motivation that the completion if this thesis became possible.
I hereby would also like to acknowledge Associate Professor Richard
Manasseh, my coordinating supervisor, who at many times inspired me
and directed me to clarify my thought processes and aided me in my
decisions.
Throughout my candidature I received many invaluable supports from
many individuals and many friendships were formed. In particular I
would like to thank Dr Michelle Dunn who introduced me to image
processing.
I would like to thank my mother, Sharareh Soufi Siavash, who taught
me how to write and read prior to attending school; stood beside me
throughout my studies; and who showered me with love and encouraged
me to grow.
Many thanks to my father, Mehran Motamed Ektesabi, who introduced
me to the field of engineering from an early age; who was always there
throughout all the hurdles of life and was there when I needed an
advice; who believed in me, motivated and inspired me to progress and
achieve my best.
My parents, you are my first and long life teachers, my best friends
and mentors, I can never appreciate you enough for all that you have
done for me. Words cannot express how I feel about you. I just hope
you accept my sincere thanks and admiration.
I would also like to thank my younger brother, Arman Motamed Ek-
tesabi, who made me laugh when I was down and showed me his per-
spectives about the importance of life.
My grandparents, each in their own way, motivated me. I hope I have
done them proud, specially my grandmother, Shirin Salimi Pirkouhi,
who would have loved to see this day but unfortunately lost her battle
to cancer. Her dream was so that I could continue my studies and it is
with her well wishes that I have reached this far. May one day, I could
take part in research for early diagnosis of cancer.
My family and friends, my most valued treasures of life, I appreciate
each and every one of you for your positive encouragements and price-
less support. You have shown me how to live and taught me about
life’s vast opportunities. I am pleased to have had the opportunity to
know you and be part of your lives.
Publications
Book Chapter:
• A. Ektesabi, A. Kapoor, ”Fringe Noise Removal of Retinal Fun-
dus Images Using Trimming Regions”, Emerging Trends in Image
Processing, Computer Vision, and Pattern Recognition, Elsevier
Inc. Jan, 2015.
Conference Proceeding:
• A. Ektesabi, A. Kapoor, ”Exact Pupil and Iris Boundary Detec-
tion”, International Conference on Control, Instrumentation, and
Automation (ICCIA), Shiraz, vol. 2, pp. 1217-1221, 2011.
• A. Ektesabi, A. Kapoor, ”Complication Prevention of Posterior
Capsular Rupture using Image Processing Techniques”, Proceed-
ings of the World Congress on Engineering 2012 (WCE 2012),
vol. I, July 4 - 6, London, U.K., pp. 603-607, 2012.
• A. Ektesabi, A. Kapoor, ”Removal of Circular Edge Noise of Reti-
nal Fundus Images”, International Conference on Image Process-
ing, Computer Vision and Pattern Recognition (IPCV’14), Las
Vegas., 2014.
• A. Ektesabi, A. Kapoor, ”Optic Disk Localisation Using Con-
secutive Adaptive Thresholding Technique”, IEEE International
Conference on Image Processing (ICIP 2016), Arizona., 2016 -
Under Review.
Abstract
The field of ophthalmology is in need of more support as it is unable
to meet the need of the growing population. This thesis considers pro-
cedures which may be used as part of an assistive telemedical tool for
aiding ophthalmologists in diagnosing wide range of ophthalmological
disorders including Cataract, Retinopathy of Prematurity and Glau-
coma, which affect more than 60% of the population worldwide. Many
different image processing techniques have been analysed and in the
process some of the most favourable and advanced systems have been
selected for identifying some of the key features of the eye which are
commonly used by ophthalmologists for disease detection.
To address this aim and create a more suitable telemedical solution,
different stages of image processing is reconsidered and enhances in
the study. The stages include, image pre-processing, feature locali-
sation and feature extraction. The aim is to create simple, fast but
universal algorithms and procedures which could be implemented on
any captured data with any specifications.
After image acquisition, the first image processing stage is the image
pre-processing. The general processes such as the colour conversion
to the gray scale and green band selection, masking the region of in-
terest and preliminary filtering for sharpening the images are initially
implemented. However, to improve the results in further stages, new
procedures such as a trimming circle to reduce fringe noise and im-
age colour enhancements are also implemented. The final results show
significant improvements and more accurate findings in these cases.
The next stage is the feature localisation stage. Previous studies have
shown the main areas of interest in retinal images are vessels, Optic
Disk and the Macula. The features are extracted using the new pro-
posed algorithms. The results are promising and the localisation is
compatible with previously conducted studies. Moreover, in this stage,
another approach is suggested resulting in the Iris and Pupil localisa-
tion. The method may be used both for biometric purposes as well as
inter-operatively in surgeries such as those of cataract.
In the feature extraction stage, different methodologies are suggested
for detecting the centre of the Iris, Pupil, Optic Disk and Macula. The
radius and the area of these features are also calculated and compared.
For vessels an approach is suggested for detecting its end points. The
use of the information may result in detection of different diseases such
as Cataract, ROP and Glaucoma.
To further assist the ophthalmologists and medical practitioners an
approach is proposed which results in mapping of the retina, which
may then be used as an aiding tool for disease diagnosis, progression
and treatment.
Lastly, to reduce the error associated with each result, the light refrac-
tion within the eye is considered and the error calculated. The error
can then be taken under consideration while analysing each result.
The outcomes of the following study suggests a reliable yet cost-effective,
simple and fast approaches in which captured eye images may be anal-
ysed as part of an automatic assistive telemedical tool.
CONTENTS
Contents viii
List of Figures xiii
List of Tables xvii
Nomenclature xxi
1 Introduction 1
1.1 Telediagnosis in Ophthalmology . . . . . . . . . . . . . . . . . . . . 1
1.1.1 Diseases and Features of Interest . . . . . . . . . . . . . . . 3
1.2 Research Question . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 Research Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.4 Proposed Methodology . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.5 Contributions of the Research . . . . . . . . . . . . . . . . . . . . . 8
1.6 Thesis Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2 Most Predominant Eye Diseases 14
2.1 Structure of the Human Eye . . . . . . . . . . . . . . . . . . . . . . 14
2.1.1 Iris, Pupil and Sclera . . . . . . . . . . . . . . . . . . . . . . 16
2.1.2 Optic Disk, Macula and Ocular Vascularization . . . . . . . 17
2.2 Visual Impairment . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
viii
CONTENTS
2.2.1 Risk Factors of Visual Impairment . . . . . . . . . . . . . . 19
2.2.2 Ophthalmological Diseases and Complications . . . . . . . . 21
2.3 Cataract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.3.1 Worldwide Effect of Cataract . . . . . . . . . . . . . . . . . 23
2.3.2 Risk Factors of Cataract . . . . . . . . . . . . . . . . . . . . 23
2.3.3 Classification and Screening of Cataract . . . . . . . . . . . 24
2.3.4 Treatment of Cataract . . . . . . . . . . . . . . . . . . . . . 25
2.3.4.1 Intracapsular Cataract Extraction (ICCE) . . . . . 25
2.3.4.2 Extracapsular Cataract Extraction (ECCE) . . . . 26
2.3.4.3 Manual Small Incision Cataract Surgery (MSICS) . 27
2.3.4.4 Phacoemulsification . . . . . . . . . . . . . . . . . 27
2.3.5 Monitoring Surgical Trainees . . . . . . . . . . . . . . . . . . 29
2.3.6 Importance of Iris and Pupil for Diagnosing Cataract . . . . 29
2.4 Retinopathy of Prematurity(ROP) . . . . . . . . . . . . . . . . . . 29
2.4.1 Worldwide Effect of ROP . . . . . . . . . . . . . . . . . . . 31
2.4.2 Risk Factors of ROP . . . . . . . . . . . . . . . . . . . . . . 31
2.4.3 Classification of ROP . . . . . . . . . . . . . . . . . . . . . . 32
2.4.4 Screening for ROP . . . . . . . . . . . . . . . . . . . . . . . 34
2.4.5 Treatment of ROP . . . . . . . . . . . . . . . . . . . . . . . 34
2.4.6 Importance of Retinal Vasculature for Diagnosing ROP . . . 35
2.5 Glaucoma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
2.5.1 Worldwide Effect of Glaucoma . . . . . . . . . . . . . . . . . 36
2.5.2 Pathogenesis of Glaucoma . . . . . . . . . . . . . . . . . . . 36
2.5.3 Risk Factors of Glaucoma . . . . . . . . . . . . . . . . . . . 37
2.5.4 Classification and Screening of Glaucoma . . . . . . . . . . . 37
2.5.5 Treatment of Glaucoma . . . . . . . . . . . . . . . . . . . . 38
2.5.6 Importance of Optic Disk and Macula for Diagnosing Glau-
coma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
2.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3 Image Processing in Ophthalmology 40
3.1 Ophthalmological Complications . . . . . . . . . . . . . . . . . . . . 40
3.1.1 Importance of Image Processing in Ophthalmology . . . . . 41
ix
CONTENTS
3.2 Image Processing Procedures . . . . . . . . . . . . . . . . . . . . . . 42
3.2.1 Image Processing in Biometrics . . . . . . . . . . . . . . . . 43
3.2.2 Image Processing in Ophthalmology . . . . . . . . . . . . . . 44
3.2.2.1 Iris and Pupil Localisation . . . . . . . . . . . . . . 44
3.2.2.2 Retinal Vessel Detection . . . . . . . . . . . . . . . 49
3.2.2.3 Optic Disck and Macula Localisation . . . . . . . . 52
3.3 Study Design Considerations . . . . . . . . . . . . . . . . . . . . . . 56
3.3.1 Examination versus Screening . . . . . . . . . . . . . . . . . 56
3.3.2 Cost Effectiveness . . . . . . . . . . . . . . . . . . . . . . . . 58
3.3.3 Image Quality . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.3.4 Accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
3.3.5 Reliability . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
3.3.6 Safety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
4 Image Acquisition and Fundus Mapping 61
4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
4.2 Image Acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.2.1 Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
4.2.1.1 Hardware Filtering . . . . . . . . . . . . . . . . . . 65
4.2.1.2 Software Filtering . . . . . . . . . . . . . . . . . . 66
4.2.2 Image Databases . . . . . . . . . . . . . . . . . . . . . . . . 66
4.3 Fundus Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
4.3.1 New Proposed Technique for Fundus Mapping . . . . . . . . 71
4.3.2 Implementation and Discussion . . . . . . . . . . . . . . . . 75
4.4 Refraction Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
4.4.1 Light Refraction In Retina . . . . . . . . . . . . . . . . . . . 77
4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
5 Image Pre-Processing 83
5.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
5.2 Image Manipulation . . . . . . . . . . . . . . . . . . . . . . . . . . 85
5.3 Masking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
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CONTENTS
5.3.1 Otsu Method . . . . . . . . . . . . . . . . . . . . . . . . . . 87
5.3.2 New Technique for Masking Using Thresholding . . . . . . . 88
5.4 Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
5.4.1 2D Fast Fourier Transform . . . . . . . . . . . . . . . . . . . 91
5.5 Sharpening the Retinal Image . . . . . . . . . . . . . . . . . . . . . 92
5.6 Trimming Regions . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
5.6.1 Circular Trimming Region . . . . . . . . . . . . . . . . . . . 97
5.6.1.1 Implementation . . . . . . . . . . . . . . . . . . . . 100
5.6.1.2 Results and Discussion . . . . . . . . . . . . . . . . 101
5.6.2 Elliptical Trimming Region . . . . . . . . . . . . . . . . . . 103
5.6.2.1 Implementation . . . . . . . . . . . . . . . . . . . . 105
5.6.2.2 Results and Discussion . . . . . . . . . . . . . . . . 105
5.7 Contrast Enhancement . . . . . . . . . . . . . . . . . . . . . . . . . 107
5.7.1 New Necessary Step . . . . . . . . . . . . . . . . . . . . . . 109
5.7.1.1 Intensity Adjusted . . . . . . . . . . . . . . . . . . 109
5.7.1.2 Histogram Equalization . . . . . . . . . . . . . . . 110
5.7.1.3 Adaptive Histogram Equalization . . . . . . . . . . 111
5.7.2 Results and Discussion . . . . . . . . . . . . . . . . . . . . . 112
5.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
6 Iris and Pupil Localisation and Extraction 116
6.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
6.2 New Technique for Iris/Pupil Localisation . . . . . . . . . . . . . . 117
6.3 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
6.4 Iris and Pupil Extraction . . . . . . . . . . . . . . . . . . . . . . . . 123
6.4.1 Center Decection . . . . . . . . . . . . . . . . . . . . . . . . 123
6.4.2 Area Calculation . . . . . . . . . . . . . . . . . . . . . . . . 124
6.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
7 Retinal Vessels Localisation and Extraction 127
7.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
7.2 Proposed Localisation Technique . . . . . . . . . . . . . . . . . . . 128
7.3 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
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CONTENTS
7.4 Retinal Vasculature Extraction . . . . . . . . . . . . . . . . . . . . 151
7.4.1 Localisation of the End Point of Vessels . . . . . . . . . . . . 151
7.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
8 Optic Disk and Macula Localisation and Extraction 154
8.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
8.2 New Technique for Optic Disk Localisation . . . . . . . . . . . . . . 155
8.3 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
8.4 Optic Disk Extraction . . . . . . . . . . . . . . . . . . . . . . . . . 162
8.4.1 Center of the Optic Disk . . . . . . . . . . . . . . . . . . . . 162
8.4.2 Area of the Optic Disk . . . . . . . . . . . . . . . . . . . . . 164
8.4.3 Cup to Disk Ratio . . . . . . . . . . . . . . . . . . . . . . . 164
8.5 Macula Localisation - Proposed Technique . . . . . . . . . . . . . . 165
8.6 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
8.7 Macula Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . 172
8.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172
9 Conclusions 174
9.1 Overall Research Program . . . . . . . . . . . . . . . . . . . . . . . 174
9.2 Research Findings, Perceived Contributions . . . . . . . . . . . . . 175
9.3 Proposals for Future Research . . . . . . . . . . . . . . . . . . . . 178
References 183
Appendix A 200
Appendix B 202
Appendix C 205
Appendix D 207
Appendix E 210
xii
LIST OF FIGURES
1.1 Stages undertaken in Image Processing . . . . . . . . . . . . . . . . 5
1.2 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.1 Chapter Two Outline . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.2 Anatomical structure of the eye . . . . . . . . . . . . . . . . . . . . 16
2.3 Regions of the eye - pupil, iris and sclera [9] . . . . . . . . . . . . . 17
2.4 Retinal fundus image where the location of the OD, macula and the
vessles are indicated. . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.5 Global causes of blindness and the percentage of affected popula-
tion [1] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.6 Template used in the Wisconsin Cataract Grading System for lo-
cating the Cataract in the right eyes. . . . . . . . . . . . . . . . . . 24
2.7 Intracapsular Cataract Extraction [48] . . . . . . . . . . . . . . . . 26
2.8 Extracapsular Cataract Extraction [50] . . . . . . . . . . . . . . . . 26
2.9 Manual Small Incision Cataract surgery [54] . . . . . . . . . . . . . 27
2.10 Phacoemulsification surgery [61] . . . . . . . . . . . . . . . . . . . . 28
2.11 Illustration of differences between normal and abnormal retinal blood-
vessel development in the child with ROP. . . . . . . . . . . . . . . 30
2.12 Classification of ROP for the left eyes [17] . . . . . . . . . . . . . . 32
3.1 Chapter Three Outline . . . . . . . . . . . . . . . . . . . . . . . . . 40
xiii
LIST OF FIGURES
3.2 Stages undertaken in Image Processing . . . . . . . . . . . . . . . . 42
3.3 Suggested Image Processing stages. . . . . . . . . . . . . . . . . . . 57
4.1 Chapter Four Outline of Image Processing Stages . . . . . . . . . . 61
4.2 Image capturing set up . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.3 Capturing device, (1) Camera, (2) Lighting, (3) Hardware light filter 65
4.4 The difference between the view angle of normal angle, narrow angle
and wide angle fundus cameras. . . . . . . . . . . . . . . . . . . . . 69
4.5 Importance of fundus mapping . . . . . . . . . . . . . . . . . . . . . 70
4.6 Geometric representation of the proposed method for merging mul-
tiple retinal images. Radius of the Curve (R), Central Angle of the
Curve (∆), Cord Length (C), Tangent Length (T ), Middle Coordi-
nate (M), External Distance (E) and the Middle (PM), Left (PL)
and Right (PR) points can be viewed in the image. . . . . . . . . . 71
4.7 Approximation of retinal curvature using the Middle Coordinate . . 74
4.8 Average light refraction indices for different regions of an eye. . . . 77
4.9 Comparison of incident ray and refractive ray - 180 degrees . . . . . 78
4.10 Comparison of incident ray and refractive ray - 90 degrees . . . . . 79
4.11 Example of bending of the refractive ray in the eye . . . . . . . . . 79
5.1 Chapter Five Outline of Image Processing Stages . . . . . . . . . . 83
5.2 Colour band separation of a coloured image with respected histograms 85
5.3 Colour component separation of RGB image in horizontal direction 86
5.4 Example of a possible mask for the sampled image . . . . . . . . . . 87
5.5 Histogram used to determine a threshold for masking the ROI . . . 89
5.6 Implementing 2D FFT on a retinal image . . . . . . . . . . . . . . . 92
5.7 Used Kernels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
5.8 Two examples of retinal fundus images. If observed closely, a bright
fringe can be seen at the left hand corner of the image (b) which
may result in inaccurate OD detection. The bright fringe cannot be
seen in the image (a). . . . . . . . . . . . . . . . . . . . . . . . . . . 95
xiv
LIST OF FIGURES
5.9 Example of results obtained for plotting a trimming region. The
green (+) signs indicate the preliminary estimated points. The
orange (+) signs indicate the calculated points, including the es-
timated center. The yellow circle is the trimming region which has
been plotted using the information. . . . . . . . . . . . . . . . . . . 101
5.10 Examples of retinal images using different capturing devices. . . . . 103
5.11 (a) Inaccurate circular trimming circle (yellow) for an elliptical
shaped captured fundus image. (b) Trimmed image. . . . . . . . . . 103
5.12 (a) Accurate circular trimming circle (yellow) for an elliptical shaped
captured fundus image using long axis as the radius. (b) Trimmed
image. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
5.13 Example of the effect of Intensity Adjustment . . . . . . . . . . . . 109
5.14 Example of the effect of Histogram Equalization . . . . . . . . . . . 110
5.15 Example of the effect of Contrast Limited Adaptive Histogram Equal-
ization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
6.1 Chapter Six Outline of Image Processing Stages . . . . . . . . . . . 116
6.2 Proposed steps for Iris and Pupil localisation. . . . . . . . . . . . . 118
6.3 Example of the possible inaccurate results obtained from two dif-
ferent Iris localisation techniques. Results from approach one and
two are outlined in green and red respectively. . . . . . . . . . . . . 119
6.4 Original image used for localisation of Iris and Pupil . . . . . . . . 120
6.5 Result obtained when localising the iris and pupil outer boundaries
using the proposed new algorithm . . . . . . . . . . . . . . . . . . . 120
7.1 Chapter Seven Outline of Image Processing Stages . . . . . . . . . . 127
7.2 Proposed steps for retinal vessel localisation. . . . . . . . . . . . . . 128
7.3 Some of the possible vessels end point using template matching . . 152
8.1 Chapter Eight Outline of Image Processing Stages . . . . . . . . . . 154
8.2 Proposed steps for Optic Disk localisation. . . . . . . . . . . . . . . 155
8.3 The gradient plot histogram used to set the threshold for the OD
localisation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156
xv
LIST OF FIGURES
8.4 Example gradient plot histograms and set thresholds for OD local-
isation for different images. . . . . . . . . . . . . . . . . . . . . . . . 157
8.5 Some possible templates for determining vessels intersection . . . . 163
8.6 Center localisation of the OD, method 1 is represented as a blue
(+) sign and method 2 as red (+) sign . . . . . . . . . . . . . . . . 163
8.7 Detection of the OC (green) and OD (red) . . . . . . . . . . . . . . 164
8.8 Different positions of macula in retinal images, in images (a) and
(d) macula is located in the center while in images (b) and (c) no
macula is present. The macula has been manually defined and can
be viewed in the images. . . . . . . . . . . . . . . . . . . . . . . . . 165
8.9 Proposed steps for Macula localisation. . . . . . . . . . . . . . . . . 166
8.10 The retinal image has been deperated into blocks. . . . . . . . . . . 167
8.11 Neural network model determining the OD block. . . . . . . . . . . 168
8.12 Complementary image. (a) Original Image, (b) Complement Image. 170
8.13 Localisation of Macula using the proposed technique. . . . . . . . . 170
9.1 Chapter Nine Outline . . . . . . . . . . . . . . . . . . . . . . . . . . 174
xvi
LIST OF TABLES
2.1 World statistics on visual impairment . . . . . . . . . . . . . . . . . 19
4.1 Refractive Index of the light passing through different regions of the
eye. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
4.2 ANOVA of the incident and refractive rays for 0-90◦ range . . . . . 80
5.1 Comparison of the masks formed by Otsu method and the suggested
new Thresholding method. . . . . . . . . . . . . . . . . . . . . . . . 90
5.2 Sharpening the retinal image using 2D FFT . . . . . . . . . . . . . 94
5.3 Comparison table of the proposed trimming circle with those sug-
gested previously in literature . . . . . . . . . . . . . . . . . . . . . 99
5.4 OD localisation using trimming circle . . . . . . . . . . . . . . . . . 102
5.5 Proposed Circular and Elliptical Trimming Regions . . . . . . . . . 104
5.6 Implementation of both circular and elliptical trimming regions for
circular and elliptically shaped retinal fundus images . . . . . . . . 105
5.7 OD Detection for Circular and Elliptical Trimming Region . . . . . 106
5.8 OD localization for contrast enhanced images. . . . . . . . . . . . . 113
6.1 Example of Iris Localisation Results . . . . . . . . . . . . . . . . . . 121
6.2 Iris localisation for different images. . . . . . . . . . . . . . . . . . . 122
7.1 Modeling and implementation of different filters for vessel detection 131
xvii
LIST OF TABLES
7.1 Modeling and implementation of different filters for vessel detection 132
7.1 Modeling and implementation of different filters for vessel detection 133
7.2 Combining results of different filters . . . . . . . . . . . . . . . . . 134
7.3 Vessel localisation for Image 1 . . . . . . . . . . . . . . . . . . . . . 136
7.4 Vessel localisation for Image 2 . . . . . . . . . . . . . . . . . . . . . 139
7.5 Vessel localisation for Image 3 . . . . . . . . . . . . . . . . . . . . . 142
7.6 Vessel localisation for Image 4 . . . . . . . . . . . . . . . . . . . . . 145
7.7 Vessel localisation for Image 5 . . . . . . . . . . . . . . . . . . . . . 148
8.1 Step by step results for OD detection, applying the proposed con-
secutive adaptive thresholding method. . . . . . . . . . . . . . . . . 159
8.2 OD localisation for different images. . . . . . . . . . . . . . . . . . . 161
8.3 Macula localisation for different images. For cases where the Macula
cannot be seen the process is stopped, such as the case for Image 6. 171
1 Angle of light as it enters the eye (Incident Ray), passes through
different interfaces within the eye and reaches the back of the eye
(Refractive Ray). . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200
2 Gray Scaled and colour component separation of coloured images . 202
3 Masks created for different images using Thresholding technique . . 205
4 2D FFT filtered images. . . . . . . . . . . . . . . . . . . . . . . . . 207
5 Sharpening the retinal images using 2D FFT filtered images. . . . . 210
xviii
NOMENCLATURE
A Area of a Circle
AC Anterior Chamber
AHE Adaptive Histogram Equalization
ALT Adaptive Local Thresholding
AMD Age-related Macular Degeneration
ANOV A Analysis of variance
BMF Binary Matched Filter
C Cord Length
CHT Circular Hough Transform
CLAHE Contrast Limited Adaptive Histogram Equalization
CNS Central Nervous System
CSLT Confocal Scanning Laser Tomography
CT Curvelet Transform
∆ Central Angle of the Curve in degrees
xix
Nomenclature
df Degrees of Freedom
DH Desired Histogram
E External Distance
F − test Fisher’s test
FDOG First Order Derivative Gaussian
FFT Fast Fourier Transform
FLDA Fisher Linear Discriminant Analysis
FOV Field Of View
GMF Gaussian Matched Filter
R Histogram Equalization
HRT Heidelberg Retinal Tomograph
HT Hough Transforme
IFFT Inverse Fast Fourier Transform
IUWT Isotropic Undecimated Wavelet Transform
KMF Kirsch Template Matched Filter
L Curve Length Distance between PI to the V ertex
M Middle Coordinate
MF Matched Filter
MS Mean Sqaures
NN Neural Networks
OC Optic Cup
OCT Optical Coherence Tomography
xx
Nomenclature
OD Optic Disk
ONH Optic Nerve Head
P Perimeter of a Circle
PACG Primary Angle Closure Glaucoma
PCA Principal Component Analysis
PCR Posterior Capsular Rupture
R Radius
RNFL Retinal Nerve Fiber Layer
ROI Region of Interest
ROP Retinopathy of Prematurity
RTA Retinal Thickness Analysis
SIM Statistic Image Mapping
SS Sum of Sqaures
SVM Support Vector Mechanism
T Tangent Length
TCA Topographic Change Analysis
UBM Ultrasound Bio-Microscopy
WHO World Health Organization
xxi
CHAPTER 1
INTRODUCTION
1.1 Telediagnosis in Ophthalmology
Vision is by far the most important sensory organ in the body and has a significant
impact on humans everyday life. With more than 284 million people who are
suffering from visual impairments, from which 39 million are legally blind [1],
ophthalmology has been an important area of research.
There are many different eye conditions and disease due to complexity of the
eye and the related organs in the visual pathway. Some of the most common
eye conditions which affect a large population are Refractive errors, Cataract,
Glaucoma, Age-related Macular Degeneration, Retinopathy and Trachoma.
If left untreated these conditions may become more severe and in some cases
lead to irreversible blindness. The cause and severity of these diseases vary depend-
ing on several factors including the age, lifestyle and environmental influences. To
improve the world’s visual acuity and reduce the lifelong effect of eye conditions,
continuous monitoring and visual inspection of the eye by optometrists and oph-
thalmologists are advised throughout one’s life. This may not be feasible for all
due to the associated costs, restrictions in the available technology and resources,
limitations in experts in developing regions and rural areas as well as the lack of
information and knowledge in some communities.
Many people who would benefit from timely correct diagnosis of uncorrected
1
1. INTRODUCTION
vision especially in regions where accessibility to the experts and resources are
limited. To achieve this and improve the health of many, for years many have
worked towards the betterment of the current ophthalmological procedures, incor-
porating knowledge from different fields. One of the areas which have proven to
have a significant positive impact in the ophthalmology and ophthalmological dis-
ease diagnosis has been image processing. Thus far, image processing applications
have been implemented in all stages of ophthalmology including image capturing,
disease diagnosis, prognosis and treatment. However, these advancements are yet
to meet the need of the growing population.
In recent times, the use of automated diagnostic system in remote locations
by a trained technician without the need of an expert on sight and with no to
minimal user input has been the drive for many investigations.
Majority of the current available diagnostic tools are limited such that these
processes are only functional when the images are of good contrast and the features
are well separated and easily distinguishable. Therefore these systems are not very
reliable or have low accuracy when the optimal conditions are not met [2].
Furthermore, these systems solely look into diagnosis of a single disease. At the
first glance, a wide range of diseases may appear to have similar characteristics and
side effects, which might be lost by an inexperienced technician or young medical
interns. Therefore, the use of these systems would limit the diagnosis and critical
information may also be lost as a result.
Another critical factor which has to be considered for a diagnostic tool is the
cost, accessibility and availability of the system in developing regions. Many de-
vices may require input information from advanced instrumentations. This may
not be possible in remote locations. Therefore this also has to be considered for
such system.
In this doctoral work, the objective has been to aid the surgeons and ophthal-
mological medical practitioners with an automated assistive tool which could be
used to detect some of the most widely affecting diseases. As a result some of the
most widely affected ophthalmological disorders such as Cataract, Retinopathy
of Prematurity (ROP) and Glaucoma which affect more than 60% of the visual
impaired population, have been investigated and their key diagnostic features de-
termined. These features were then determined by suggesting several image pro-
2
1. INTRODUCTION
cessing approaches. The fact that a broad range of features and diseases have been
considered in this case opens up the opportunity for a single diagnostic tool to be
used by all ophthalmologists for broad range of disease diagnosis. This diagnos-
tic tool may also be used as the initial tool for separating the high risk patients
from the normal population. These patients can then be referred to the medical
specialist.
1.1.1 Diseases and Features of Interest
In order to resolve any problem, it is crucial to have an in-depth awareness of
its background information such as its cause, short and long term effect, current
available solutions, appropriateness and reliability of those solutions, and possible
new approaches.
In this case, prior to studying current available techniques and proposing new
ones, detailed understanding and defining ophthalmological diseases, their cause
and impact in the world, and key features of detection is important. As each
disease affects a certain region of the eye, it is essential to investigate those regions.
However, with such a broad range, all aspects could not have been covered in this
study. Since majority of diseases affect the retina in one way or another, studying
retina has been the main area of interest in the performed research. A selected
number of eye and retinal features were chosen for further investigation.
Iris and Pupil have been chosen to aid ophthalmologists in diagnosis and treat-
ment of Cataract which mainly occurs in the older population. At the occurrence
of Cataract, there is a change in shape and clarity of the eye lens. The lens is
visually inspected through the Iris and Pupil. Hence detecting the Iris and Pupil
and monitoring the changes can be useful in complication avoidance. Furthermore,
the proposed procedures for Iris detection can also help in biometrics applications
which have been used globally for security purposes.
Vessels are the most important key feature of the retina as many different
diseases directly influence the appearance and growth of the retinal vasculature.
One of such conditions is the blinding disease of ROP, which affects premature
infants. The life threatening impact of this disease is very significant as it can
influence the lives of the patients, their families as well as the society. As a result,
3
1. INTRODUCTION
studying the vessels and their growth could be beneficial for these patients. In
this study, the vessels and their end-points have been detected to help the lives of
these patients.
Another leading ophthalmological disorder in the world affecting the ageing
population is Glaucoma. It is known to impact the shape of the Optic Disk (OD).
Therefore, studying the OD and its variation in shape can have a major impact
on the patients who might be suffering from this disease. The OD and Macula of
the eye have been investigated and the features extracted in order to help classify
diseases such as Glaucoma.
Despite the fact that each of the features may be used individually to detect
or determine the progression of a specific disease, there are times where the com-
bination of the results obtained from analysing these features would reveal more
information. This would result in an increase in the accuracy and assurance of
disease classification.
The features can also aid in development of a fundus map as their location
would be used as a marker. With an increase in the number of markers, the errors
associated with the mapping decreases and so the precision of the prognosis made
by ophthalmologist significantly improves.
Detection of each individual feature has been proposed by many researchers
but none have considered accuracy of detection by combination of few features.
For the purpose of this study, new methodologies are proposed to determine all
the key features of the eye using image processing.
1.2 Research Question
This study has concentrated on developing and improving the current image pro-
cessing techniques in order to assist ophthalmologists in their preliminary stages
of diagnosis of diseases such as Glaucoma, ROP and Cataract by extracting infor-
mation from the key identifiers of these diseases.
Thus far, visual analyses performed by ophthalmologists have been the best
processing tools for image analysis in disease prognosis. With the recent advance-
ments in image capturing devices, more information and intricate details have been
revealed by the images. With the aid of the computer vision and image processing
4
1. INTRODUCTION
approaches many of these information have been detected and outlined in order
to assist the medical practitioners in their prognosis. Due to non-invasiveness,
functions, reliability and accuracy of the image processing procedures, they have
been of interest by many ophthalmologists globally.
Many studies have been performed towards these ophthalmological tools. How-
ever, the limitations such as the availability of resources, experts, costs, accessibil-
ity and affordability of the devices have restricted the research in the developing
regions or remote locations in developed countries where it is needed most.
In developed countries, the use of the computer vision tools to analysis eye
images in combination with ophthalmological expertise have shown to be more
successful in disease diagnosis and therefore have been of great interest. To enhance
the precision, achieve the desired consistency in the results and the anticipated
automation further improvements are needed.
In order to propose new methodologies which may be incorporated as part of
this telemedical ophthalmological tool, as illustrated in flowchart shown in Fig-
ure 1.1 the main stages of image processing have been considered. The suggested
techniques have to be of high accuracy, adaptable and simple so that they could
be used as part of this tool offsite in remote and rural locations and onsite, by
medical practitioners.
Eye Image Acquisition Image Processing Interpretation Display/Transmission/Storage
Figure 1.1: Stages undertaken in Image Processing
1.3 Research Objectives
The overall objective of this research has been to work towards aiding medical
practitioners in the disease prognosis with a single, cost-effective, diagnostic as-
sistive tool for detection of Cataract, ROP and Glaucoma. In both developing
and developed countries, there has always been the need for such a device due to
limited amount of expertise, instrumentations and resources in remote rural areas.
5
1. INTRODUCTION
The key objectives of this study were to study and examine all stages of im-
age processing and suggest new methodologies to improve the overall results for
ophthalmological diagnosis:
• Image Acquisition stage:
– Create a fundus map to enhance the field of view by using multiple
retinal images
– Study the effect of light refraction
• Image Pre-processing stage:
– Enhancing the contrast
– Introducing additional trimming region
• Feature Detection and Extraction Stage in order to assist ophthalmologists
in their diagnosis stage in particular for wide spread diseases of Cataract,
ROP and Glaucoma:
– Cataract - Detect Iris and Pupil and extract information by locating
the center and calculating the area
– ROP - Detect the retinal vessels and localise the end-points
– Glaucoma - Detect the OD and Macula and extract information by
detecting the center and calculating the area
In order to work towards the objectives, the following factors were also consid-
ered:
• Working towards creating a cost-effective solution for diagnosing diseases in
remote or rural areas
• Increasing the overall accuracy of the image processing procedures
• Suggesting reliable and robust approaches
• Considering the non-invasiveness and safety of the procedures
• Improving the processing time
6
1. INTRODUCTION
1.4 Proposed Methodology
Once the images have been obtained, the image processing may take place. As
mentioned previously, localisation of the features of the eye; iris, pupil, OD, macula
and vessels; have been the basis of the study.
Since majority of the underlying image processing procedures are the same in
all cases, they may be performed once and the output used for further stages.
The first stage in image processing is pre-processing. In the literature, many
different procedures have been suggested, some of which have been chosen and
implemented. However, to further enhance the results, this study has shown
that other procedures such as, redefining the masking region via implementing
a trimming region, as well as enhancing the contract of the images would result
in better outcomes. Therefore, the proposed procedures are also performed in the
pre-processing stage.
Once the images have been prepared and manipulated, the main features of
the eye may be localised in the feature localisation stage. For each case, based on
the features specification, a methodology has been proposed.
The iris and pupil localisation has been performed by the suggested method-
ology of implementing two methods of thresholding and active contour procedure
simultaneously and combing the results to enhance the outcome. The vessels have
been localised using the proposed method of applying edge detection on the 2D
Fast Fourier Transform filtered image. The positions of the OD and macula have
been found using the novel approach of consecutive adaptive thresholding.
This is then followed by extraction of features such as endpoint of the vessels,
the radius of the OD or localisation of its centre.
For a more reliable and practical result, an error study has been performed in
order to better match the approximated results with those of the real life.
To achieve the overall purpose of telemedicine, a new procedure has also been
suggested for fundus mapping, revealing more information to the medical experts
in regards to patients’ health.
All the information obtained and procedures performed can then the displayed
to aid the ophthalmologists in the disease classification.
7
1. INTRODUCTION
1.5 Contributions of the Research
The conducted research, concentrated on incorporating image processing into the
field of ophthalmology. Different stages of image processing, interpretation and
displaying results; their importance and approaches have been considered and new
methodologies suggested for each stage, complementing the available techniques.
The image processing section has been separated into four subsections of image
acquisition, image pre-processing, feature localisation and extraction. The main
contributions of the project are explained briefly in the following:
1. Image Acquisition:
In this stage the best image has been captured and set for the consequtive
sections.
• Light Refraction:
Image capturing procedure is not ideal; moreover, in majority of cases
the refraction of the light has also been ignored. As a result, the errors
associated with light refraction has been calculated and suggested to be
taken under consideration for future studies.
• Fundus Mapping:
In the study, a new approach in creating a fundus map has been sug-
gested, improving the accuracy of the current available procedures.
2. Image Pre-Processing:
In the image pre-processing, in conjunction with the implementation of the
previously proposed techniques in the literature, further modification have
also been recommended.
• Trimming Regions:
New trimming regions have been proposed to remove the bright regions
around the image, which have been caused by the ambient light. The
circular or elliptical trimming region have proven to increase the accu-
racy of detection in particular for the localisation of the OD.
8
1. INTRODUCTION
• Contrast Enhancement:
Since the images have been obtained from different instrumentations
and also the responces of each patient varies from one another, it is
important to enhance the contrast of the image. It has been suggested
to implemnt the further processes on both the original raw data as well
as the contrast enhanced data so that the performance is more accurate.
3. Feature Localisation:
For the second stage of image processing, feature localisation, features such
as iris, pupil, vessels, OD and macula have been detected.
• Iris and Pupil:
For the case of iris and pupil, the use of combination of two readily
available methodologies from the literature has been suggested. Once
the procedures were implemented concurrently, two separate masks were
obtained. Overlapping the masks ensures that the interferences are
reduced and the regions of interest are detected with a higher precision.
• Vessels:
For vasculature detection, different methodologies were considered. Ves-
sels are detected by performing the 2D Fast Fourier Transform filtering
and edge detection filters.
• Optic Disk and macula:
The OD and macula were localised using a novel approach of consec-
utive Adaptive Thresholding technique. In the case of the OD, the
brightest regions were considered, while for macula, the dark regions
were of interest. This technique proved to accurately detect the re-
gions in comparison to the previously suggested approximations from
the literature.
4. Feature Extraction:
The fourth section of the image processing sections is the feature extraction.
In this study, endpoints of the vessels, radius, centre and area of the Optic
9
1. INTRODUCTION
Disk, Macula, Iris and Pupil are calculated. The results would then aid in
interpretation section.
• Endpoints of the Vessels:
In some diseases such as the ROP, determining the endpoints of the
vessels are of great importance as they indicate the regions where the
treatment is required. These points have been detected using the neural
network concept.
• Centre and Radius:
For the centre localisation, two different approaches are suggested. The
first one considers the centre as the middle value of the detected bound-
aries from the feature localisation stage. However, for the case of OD,
the centre may also be located as the origin of vessel formation.
Once the centre is estimated, the radius can then be determined by
calculating the distance between the centre and the boundary of the
detected region.
• Area:
Two different approaches were suggested to calculate the area. The first
approach was to use the calculated radius and apply it to the equation
for the area of the circle to approximate the result. The second approach
was to determine the area using the perimeter of the detected region.
1.6 Thesis Structure
This thesis is organised as:
1. Introduction:
Chapter 1 has been reviewing and introducing the study, considering the
overall purpose of the research. In this chapter, the importance of image pro-
cessing applications in ophthalmological telemedicine has been highlighted.
The research objectives have been defined as assisting ophthalmologists by
improving the diagnosis process of widely affecting diseases of Cataract, ROP
10
1. INTRODUCTION
and Glaucoma. The undertaken methodologies, achievements and contribu-
tions to the field of study have been briefed, outlining the structure of the
thesis.
2. Literature Review:
Prior to the introduction of the proposed methodologies, it is of importance
to study the key areas which are to be detected and studied with the as-
sistance of the image processing technique. Examining the current state of
the art image processing procedures and determining the advantages and
disadvantages of the current approaches would also assist in determining the
areas in need of further improvements.
Chapter 2 introduces some of the ophthalmological diseases such as Cataract,
ROP and Glaucoma, which impact a wide population globally. Their cause,
impact on future generations and importance of early detection and treat-
ment has also been reviewed. The main key features which ophthalmologists
use to detect these conditions have been identified and aimed to be detected
using image processing in the consecutive chapters.
Chapter 3 reviews the important advances in image processing approaches.
The past and current applications have been considered so as the impact of
image processing in the field of medicine in particular in ophthalmology. The
previous literature identifying the key features defined in chapter 2 has also
been reviewed in this chapter. This has then been followed by the general
outline of the image processing procedures undertaken in the study.
3. Contributions of the Research:
In the remaining chapters, image processing techniques have been imple-
mented to detect the features. It should also be noted that in order to im-
prove or resolve any procedure, it is essential to gain vast knowledge about
the extent of the problem as well as the downfalls of the techniques used,
which has been considered throughout the study and each section refers to
a different part of the literature.
Chapter 4 considers the image acquisition and its quality. Image capturing,
Fundus mapping and the impact of light refraction on the findings have
11
1. INTRODUCTION
been considered in this chapter. For the image acquisition section of the
study, open source images have been attained. To improve the precision of
the study, it is necessary to study the light refraction and bending of light
within the eye. Majority of the studies performed previously seem to have
overlooked this crucial fact. the actual angle at which the light enters and
reaches the back of the eye has been calculated and the precision of the
accuracy is determined. Moreover, using similar concept, a new approach
has been suggested in forming a fundus map which may be used to assist the
experts’ decision further, via providing them with a wider view of the retina.
Chapter 5 proposes and implements different pre-processing approaches.
This includes the stages such as image manipulation, masking the region of
interest and filtering. To further improve the accuracy of detection, modifi-
cations such as contrast enhancement and trimming region for noise removal
had been suggested.
Once the image have been prepared, the key features can then be detected.
It should be noted that each feature would have its own specifications and
characteristics which can be used to distinguish them from one another. The
next three chaptes considers the feature localisation and extraction stages of
image processing
Chapter 6 presents the proposed approach for detection of Iris and Pupil,
which may be used as part of Cataract diagnosis or biomedical applications.
Chapter 7 looks into the localisation and extraction of retinal vasculature.
The findings can be used for detection and treatment of diseases such as
ROP.
Chapter 8 considers the image processing approaches which may be used
for detection of OD and Macula. These features are the identifying regions
of diseases such as Glaucoma.
4. Conclusion:
Chapter 9 is the conclusion and possible future contributions in the field
of ophthalmology. In this chapter, the overall contributions have been high-
lighted. Moreover, since technology is a growing field and with forthcoming
12
1. INTRODUCTION
advancements the current techniques may enhance further, potential im-
provements have also been suggested for future reference.
In the flowchart depicted in Figure 1.2, the overall outlay of image processing
stages undertaken in the study is illustrated and the content of each chapter is
shown.
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
Introduction
Eye
Diseases:
— Cataract
— ROP
— Glaucoma
Image Processing in Ophthalmology
Image Acquisition Image Databases
Fundus Mapping
Refraction Study
Image Pre-Processing
Implementation:
— Colour Separation
— Masking ROI
— Filtering/Noise Removal
— Image Sharpening
Further Modification:
— Contrast Enhancement
— Trimming
ROPFeature Localisation:
— Retinal Vasculature
Feature Extraction:
— End-point
Cataract
Feature Localisation:
—Iris
— Pupil
Feature Extraction:
— Center
— Area
Glaucoma
Feature Localisation:
—OD
— Macula
Feature Extraction:
— Center
— Area
Conclusion
Figure 1.2: Thesis Outline
13
CHAPTER 2
MOST PREDOMINANT EYE DISEASES
Introduction Literature Review
Most Predominant Eye Diseases Image Processing in Ophthalmology
Thesis Outline Conclusion
Figure 2.1: Chapter Two Outline
2.1 Structure of the Human Eye
Humans have five main sensory organs from which the vision is by far the most
important sense throughout their lives. From the moment a child is born, he/she
would start learning by observing the surroundings.
Vision has a great impact in all aspects of one’s life. Describing or distin-
guishing an object would be by far easier if the object is viewed. Imagining and
dreaming becomes more realistic if there is a visual perception behind it. Being
able to see objects would also allow individuals to easily move in their surround-
ings or do all sort of different tasks such as being employed without limitations in
their desired field.
However, currently in the world there are a large number of people who are
14
2. MOST PREDOMINANT EYE DISEASES
visually impaired. This has significant effect in their everyday lives and restricts
them to a certain extent. Moreover, the substantial social implications associated
with these complications would have irreversible emotional impacts in their lives.
There will also be a greater need for extra special resources, facilities and staff
to meet the needs of the visually impaired population resulting in more financial
liability on the governments and the communities. It is therefore essential to reduce
the number of incidences of the complications and patients.
Over the past few decades, with an increased interest in ophthalmological re-
search, there have been significant improvements in ophthalmic disease diagnosis
and treatments, which has reduced the number of severe cases and irreversible
blindness. However, with the growing population there is still need for further
studies and developments, in particular incorporating the biomedical engineering
advancements into the field of ophthalmology.
This thesis will introduce the novel image processing methodologies to extract
key features of interest for ophthalmological disorders. However, prior to doing
so, it is important to know more about the underlying problems, the requirements
and how the image processing applications could aid the medical practitioners in
resolving these issues.
This chapter will discuss a detailed overview of the field of ophthalmology, in
depth study of selected number of ophthalmological major complications, their
categorization and diagnosis. To have a better understanding of these concepts, it
is essential to have a better understanding of the eye and its underlying structure.
Eye is an important sensory organ in the body. A great amount of information
received and processed by human beings is acquired through the eyes.
The human eye is a spherical shaped structure. On average radius of the eye
is about 12mm and the length of the pupillary axis1 is between 23-25mm [3, 4].
Human eye comprises of six main regions: cornea, aqueous humour, iris, lens,
vitreous humour and sclera. The other ocular domains consist of retina and
choroid [3, 5]. Some of these features, including the clear curved cornea, the
colored iris, protective lid, eyelashes, pupil and sclera [6] can easily be viewed by
the naked eye.
1Pupillary axis is the distance between the cornea and the posterior region of the eye.
15
2. MOST PREDOMINANT EYE DISEASES
Figure 2.2: Anatomical structure of the eye
Figure 2.2 represents the major regions of the eye [7], each having their own
specific function.
For the purpose of this study, only certain features of the eye are of interest,
including the Iris, Pupil, and other retinal layer internal structure. In the following
section, these features, their structure and biological characteristics are discussed
briefly.
2.1.1 Iris, Pupil and Sclera
Iris is the pigmented structure of the eye and so determines individuals eye colour.
The black circular region surrounded by the Iris is called Pupil which is the opening
of the eye. Iris and Pupil are shown in Figure 2.2 and 2.3.
Based on the Iris, the eye is separated into two regions of the anterior and
posterior regions. The structures in front of the Iris are classified as an anterior
ocular region and the structures behind it are classified as the posterior ocular
region [3, 8].
The white outer layer surrounding the iris is the sclera which continues ante-
riorly into the cornea. The sclera is the connective tissue layer which appears as
the white outer layer of the iris. Due to the intraocular pressure, it has a rigid
structure which allows it to support the eye under muscular control and keep the
16
2. MOST PREDOMINANT EYE DISEASES
optical length constant [6].
Another major function of the iris is that it controls the change in the diameter
of the pupil based on different ambient lighting conditions. The iris consists of the
stroma1 which connects to the sphincter pupillae2 and dilator pupillae3. Using this
structure, it can control the size of the pupil. In low light intensity conditions, the
pupil of the eye dilates to about 9mm while at the luminous conditions it shrinks
to about 1mm [3].
Figure 2.3 illustrates the location of the iris, pupil and the sclera of the eye [9].
Figure 2.3: Regions of the eye - pupil, iris and sclera [9]
2.1.2 Optic Disk, Macula and Ocular Vascularization
As it can be viewed in Figure 2.2, retina is the innermost layer of the eye. Since
retina and the optic nerve originate as the outgrowths of the brain during the em-
bryonic development, they are parts of the central nervous system (CNS). Retina
is also the only part of the CNS which can be imaged directly [3, 10].
Using neural cells, the retina, which is a very light sensitive layer, can transform
light energy into neural signals [3, 7]. The signals are then transmitted into the
brain through the optic nerve head into the optic nerve. The optic nerve is also
commonly known as the Optic Disk (OD). All the central retinal vessels and the
arteries of the eye enter through the trunk of the OD.
It should also be noted that about 80% of the ocular blood flows occurs in
the choroid, which is in the mid layer of the eye and has a highly vascularized
structure [11].
1Stroma is a fibrovascular tissue.2Sphincter pupillae are constricting muscles.3Dilator pupillae are dilator muscles.
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2. MOST PREDOMINANT EYE DISEASES
Another region within the retinal of the eye is the macula. Due to the presence
of the carotenoid pigments in the retina and the pigment granules in the retinal
pigments epithelial layer beneath the retina, the macula usually appears darker
than the neighbouring tissue. It should also be noted that the macula’s structure,
size and pigmentation may vary greatly across individuals [11].
Figure 2.4 shows the position of the OD, macula and the vasculature of the
eye.
Figure 2.4: Retinal fundus image where the location of the OD, macula and thevessles are indicated.
2.2 Visual Impairment
Based on the studies performed by World Health Organization (WHO) in 2011,
there are about 284 million people in the world who are visually impaired. This
includes the 245 million people who have low vision, which means that they have
moderate or severe visually impairment. Unfortunatelty, the remaining 39 million
people have irreversible visual impairment and are blind [1, 9, 12]. The leading
causes of blindness include Cataract, Glaucoma and Age-related Macular Degener-
ation (AMD). Table 2.1 indicates the world statistic on vision which was obtained
by WHO [1].
18
2. MOST PREDOMINANT EYE DISEASES
Table 2.1: World statistics on visual impairment
Population (Million)
World 6.8× 103 [13]
Visually Impaired 285
Low Vision 246
Blind 39
Blinded by Cataract 18
Blinded by ROP 0.05
Blinded by Glaucoma 50
The significant number of people who are visually impaired has been the main
reason for the wide range of studies performed in the field of ophthalmology, since
loss of vision can be tragic for humans as it would present challenges both for
individuals and the society. Therefore, Saving and restoring vision has been the
main desire and drive for many biomedical and ophthalmological researchers for
better understanding of the eye and its function [6].
2.2.1 Risk Factors of Visual Impairment
Majority of the ophthalmological diseases are multifactorial in the origin. The
factors such as the ocular structure, function, ethnic group, inheritance, location,
life style , presence of other health conditions, age and gender could be the cause
of diverse range of diseases [1, 14].
Statistics reveal that about 87% of the visually impaired live in developing
countries, which indicates that one’s lifestyle, income and the availability and
access to resources could increase the chances of individuals being visually im-
paired [1, 15].
Ones diet and life style can also affect the chances of them being visually
impaired. Factors such as alcohol consumption may increase the chance of occur-
rences of diseases such as Cataract. Several studies have indicated that although
19
2. MOST PREDOMINANT EYE DISEASES
moderate alcohol consumption may decrease the chances of Cataract formation,
excess drinking may increase the chance significantly [16].
Studies performed by WHO has also indicated that all over the world, the risk
of visual impairment in females of all ages is higher than the males [1].
Studies have shown that the most common factor for majority of visual impair-
ments is age. 19% of the world population consists of people over 50, from which
82% are visually impaired [1]. With the growing population and the increase in life
expectancy the chances of occurrences of diseases would also increase, in particular
for age related diseases such as AMD and Glaucoma [14].
Other diseases such as Retinopathy of Prematurity (ROP) can also be age
related. The number of incidences of this disease varies depending on the birth time
of the infant. Significant increase in number is observed for infants who are born
before 31 week of gestation or are weighing less than 1250 grams [17, 18, 19, 20].
Visual impairment in children is another area of concern, with more than 12
million children between the ages of 5-15 being visually impaired due to uncor-
rected refractive errors (near sightedness, farsightedness or astigmatism). From
this, 1.4 million children are blind. The lifelong effect, resources and complica-
tions that the children are facing is tremendous and would have an impact on
them as well as the society [1].
What is interesting is that the studies have indicated that about 85% of the
visually impairment in the world could be avoided [1] if:
• Healthcare services are improved, increased and made more affordable.
• Further research and studies are performed for cure and prevention of oph-
thalmological complications by the national leaders, medical professionals
and private and corporate partners.
• The general population becomes more aware and educated about the avail-
able health care services.
• The infectious causes of vision loss is eliminated via effective eye health
strategies.
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2. MOST PREDOMINANT EYE DISEASES
2.2.2 Ophthalmological Diseases and Complications
There are many different eye conditions. The most widely affected diseases which
result in visual impairment include Cataract, Glaucoma, AMD and Uncorrected
refractive errors. Other diseases affect smaller number of people in the society,
a few examples could include ROP, Diabetic retinopathy, Corneal opacity and
blinding trachoma. Figure 2.5 depicts the percentage of the population affected
by these diseases based on the statistics obtained by WHO in 2011 [1].
Corneal Opacity5%
Diabetic Retinopathy
5%
Childhood Blindness
4%
Trachoma
3%Cataract
48%
Other
14%Glaucoma
12%
Age-Related Macular Degeneration9%
Figure 2.5: Global causes of blindness and the percentage of affected population [1]
As it can be seen in the figure 2.5, Cataract is the leading cause of visual
impairment in the world. It causes about 48% of the blindness especially in the
developing countries where about 18 million people are blinded by Cataract [21,
22, 23]. Cataract occurs when the protein structure within the lens is denatured,
resulting in clouding of the lens and impeding the light from passing through it.
Later on in this chapter, more details regarding the cataract, its aetiology and
stages will be discussed.
Another condition which affects a significant number of people is Glaucoma. It
affects the ganglion cells and their axons, and as a result it alters the topography
of the OD [24, 25]. In depth review of Glaucoma will be provided in Section 2.5.
AMD is an age related disease which affects the macula and results in loss of
21
2. MOST PREDOMINANT EYE DISEASES
central vision [26]. It is one of the main ophthalmological complications since with
an increase in the growing elderly population; the number of AMD also increases.
Another disease of interest is the ROP which occurs in the premature infants.
It affects the vessel formation in the retina as a result of variation in oxygen
consumption [27]. ROP has been covered in more details in this chapter.
Studies performed by Taylor et.al. indicated that in 2004 in Australia, 480,300
people had low vision, including the 50,600 people who are blind with the numbers
excepted to double by 2024. The common causes of the blindness were found to
be uncorrected refractive error which counted for 62%, Cataract, 14%, and AMD
10%. Moreover it was suggested that about 76% of the uncorrected refractive error
and cataract could have been avoided and treated if detected early [28]. Therefore,
it can be seen that vision loss even in Australia as a developed country is a critical
problem and should be further examined.
In this study, some of the diseases including the Cataract, Glaucoma and ROP
are chosen for further investigation. For each of them, in the following section,
a brief history, the cause and biological pathogenesis, risk factors, classifications,
screening and treatment is explained in more details.
2.3 Cataract
Cataract is one of the leading vision loss disorders in the recent times. Cataract
is a condition where the protein structure within the crystalline lens of the human
eyes is denatured, causing its opacity to change, appearing to be cloudy. This
opacification obstructs the path of the light entering the eyes, blurring the patients’
vision [23, 29].
The major leading factor in cataract formation is age, since with aging the
protein structure within the lens of the person starts binding or cross linking with
one another, becoming stiffer and forming cloudy spots [29, 30].
Patients with cataract experience range of visual deficits including: detrition
of visual acuity, problems under glare condition, altered colour recognition, loss of
contrast sensitivity [31, 32]
In the past few decades there has been an increase in the rate of cataract in the
world even in the western countries. Due to its negative effects in health related life
22
2. MOST PREDOMINANT EYE DISEASES
qualities such as difficulty in visual associated daily activities, impaired physical
performance, reduced health status and associated costs, it has become an area of
concern [33]. Therefore, many researchers have concentrated on cataract, its early
diagnosis and treatment.
2.3.1 Worldwide Effect of Cataract
The impact of cataract globally is very significant. It has been found that cataract
is the leading cause of blindness worldwide, accounting for 39.1% of the total
blindness if refractive error’s statistics is considered and 47.8% of blindness if the
refractive error’s statistics is excluded. This suggests that cataract by itself is the
cause of blindness in more than 17.7 million people [22] in both developing and
developed countries, with the number increasing each year [21].
Lawani et.al. and Agarwal et.al. have found that in developed countries,
cataract is the cause of loss of sight in 5% of the population, while in developing
countries it is responsible for more than 50% of the blindness [34, 35].
2.3.2 Risk Factors of Cataract
There are several risk factors [16, 22, 36] associated with cataract formation, some
of which include:
• Age - Increasing age would increase the change of cataract formation.
• Gender - Females have higher tendency in contracting cataract.
• Life style - Higher chance of cataract if living in warmer, sunnier climates,
engaged in outdoor activities.
• Latitude - people living in northern latitudes are more likely to have cataract.
• Income - The low to middle income families especially in developing countries
has a higher chance of having cataract.
• Ultraviolet light - People living in regions with higher ambient ultraviolet
light have higher risk of cataract.
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2. MOST PREDOMINANT EYE DISEASES
• Alcohol consumption - Although moderate drinking reduces the chances
of cataract formation but high alcohol consumption increases the risk of
cataract formation such as the posterior sub-capsular cataract.
2.3.3 Classification and Screening of Cataract
Accurate diagnosis and treatment of cataract is essential and can prevent vision
loss. The preliminary stage in treatment is to precisely categorize the cataract and
based on that choose the appropriate treatment method. The ophthalmologists
have to examine the Iris and Pupil of the eye to determine the existence of Cataract.
Based on the location of the development of the cloudy spots, the cataract is
categorized into three types [29, 37]:
1. Posterior subcapsular Cataract - occurs at the back of the lens
2. Cortical Cataract - occurs at the lens cortex and extends its spokes from the
outside to the center of the lens
3. Nuclear Cataract - is the most common type of the cataract and occurs in
the nucleus
Measuring the severity of the cataract is also important for choosing the right
treatment methodology. Several different techniques for quantification of severity
of cataract have been suggested in the literature [29, 38, 39, 40, 41, 42] includ-
ing a very common technique of Wisconsin Cataract Grading System [38]. Fig-
ure 2.6 shows the grid used in the Wisconsin Cataract Grading System for defining
cataract location in the right eyes.
Figure 2.6: Template used in the Wisconsin Cataract Grading System for locatingthe Cataract in the right eyes.
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2. MOST PREDOMINANT EYE DISEASES
2.3.4 Treatment of Cataract
The common treatment for cataract is performing the cataract surgery which is
highly effective and results in somewhat immediate visual rehabilitation. It im-
proves the visual acuity of patients considerably. The most common cataract
surgery types include:
1. Intracapsular Cataract Extraction (ICCE)
2. Extracapsular Cataract Extraction (ECCE)
3. Manual Small Incision Cataract Surgery (MSICS)
4. Phacoemulsification
The impact of cataract surgery is very significant in an individual’s life as it
provides them a better chance for performing daily activities, as well as improving
their social and emotional life components. The evidence also suggests that the
surgery improves the visual functions in co-morbid eye conditions especially if
performed in the early stages of disease [23].
Although the cataract surgery has proven to be very successful and cost-
effective, its performance in developing countries are somewhat challenging [22,
35, 43]. Moreover with the advancements in technology, patients’ expectations
will undoubtedly increase in the future [21].
Since the scope of the project does not require the detailed procedure of differ-
ent types of cataract surgeries, they have been briefly explained in the following.
The following literatures maybe referred to for more information [44, 45, 46, 47].
2.3.4.1 Intracapsular Cataract Extraction (ICCE)
The initial surgical technique to treat cataract was Intracapsular Cataract Extrac-
tion (ICCE). In the ICCE a large incision is used to remove the entire natural lens
of the eye, including its capsule.
This technique was unable to correct refractive error and so the visual recovery
was not sufficient [22]. Over the years the use of this technique has declined with
the introduction of ECCE procedure and the use of Intra-Ocular Lens (IOL).
25
2. MOST PREDOMINANT EYE DISEASES
The associated steps in the Intracapsular Cataract Extraction has been shown
in the Figure 2.7.
Figure 2.7: Intracapsular Cataract Extraction [48]
2.3.4.2 Extracapsular Cataract Extraction (ECCE)
Extracapsular Cataract Extraction (ECCE) procedure has shown to be more suc-
cessful and result in better visual outcome and quality of life in comparison to
the ICCE method [22]. In this surgery, the incision size is smaller in comparison
to the ICCE, about 8-12mm, and only the lens is removed while the capsule is
untouched [49].
The Extracapsular Cataract Extraction has been shown in the Figure 2.8.
Figure 2.8: Extracapsular Cataract Extraction [50]
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2. MOST PREDOMINANT EYE DISEASES
2.3.4.3 Manual Small Incision Cataract Surgery (MSICS)
Manual Small Incision Cataract Surgery (MSICS) is the most commonly used
technique in developing countries and it surfaced literature in the early 1990s [51].
In the MSIC process, the lens is removed as a whole through a self-sealing scleral
tunnel wound [52]. The wound does not require any sutures and is smaller than
the ECCE surgery, about 6.5mm [53].
Figure 2.9, represents the steps in Manual Small Incision Cataract surgery for
Cataract removal.
Figure 2.9: Manual Small Incision Cataract surgery [54]
MSICS is more cost effective, has faster rehabilitation and would result in bet-
ter visual acuity in comparison to the ECCE [21, 22, 35]. However, in comparison
to the Phacoemulsification technique the outcome of the surgically induced astig-
matism is higher in MSICS [21] and it may lead to several post-operative refractive
errors [22]. Overall the visual acuity of the phacoemulsification has proven to be
better in comparison to other available techniques [55, 56].
2.3.4.4 Phacoemulsification
Phacoemulsification is the most modern technique in Cataract surgery. It refers
to the procedure were the lens is divided into pieces and emulsified by an ultra-
sonic surgical handpiece. The pieces are aspirated out with the chamber fluid.
The anterior chamber pressure is kept constant via irrigation of the balanced salt
solution [57].
The ultrasonic surgical device currently used in phacoemulsification was first
introduced by Kelman in 1967 and has improved extensively since [58, 59, 60].
Figure 2.10, illustrates the Phacoemulsification surgery for Cataract removal.
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2. MOST PREDOMINANT EYE DISEASES
Figure 2.10: Phacoemulsification surgery [61]
Some of the advantages of phacoemulsification technique [21, 55, 62] over other
available techniques could be as follow:
• Smaller incision size - 1.0 ± 0.12 mm
• Less invasive - Smaller incision size, no sutures
• Short surgical time - about 10 minutes
• Less surgically induced astigmatism
• Less leakage of fluids - Type and direction of the incision as well as the blades
used ensures that the anterior chamber fluid leakage is minimal.
• Rapid recovery
• Better visual acuity - Corrected vision as a result of lens replacement
The most recent advancements in phacoemulsification are that the needle
tip vibrates longitudinally and horizontally at frequencies ranging between 28-
50 kHz [59]. As a result the patients are exposed to low frequency ultrasonic
energy and the heat it may produce. It should also be noted that since the heat
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2. MOST PREDOMINANT EYE DISEASES
may damage the corneal endothelium and hence affect the overall surgical out-
come, the exposure time and average power of the device should be monitored
constantly [60].
2.3.5 Monitoring Surgical Trainees
Majority of the surgeons’ today use computer based training tools as a preliminary
basis for performing Cataract surgeries. However, with only a few hours of training,
their level of expertise may not be sufficient when it comes to real life complications.
Moreover, majority of the performed surgeries are also subjective with no ref-
erence to a validated standard. They are based on the surgeons’ experience. The
limited experience or severity of the surgical complication could result in the life-
long side effects in patients’ life.
As a result it is essential to have a monitoring system to see the overall progress
of the surgeons. This system can also be used as an assistive tool to train and
guide the surgeons through the surgery. For the case of Cataract, the first step in
creating this device is to exactly locate the Iris and Pupil which has been further
investigated in the following chapters.
2.3.6 Importance of Iris and Pupil for Diagnosing Cataract
Based on the above factors, locating the Iris and Pupil during the surgery can
be used to determine whether a complication has occurred or not. This can be
achieved by studying the extent of the variation which occurs in shape of the Iris
and Pupil.
2.4 Retinopathy of Prematurity(ROP)
Approximately about 1% of the neonates are born prematurely, with a birth weight
below 1,500g, while roughly about 0.5% weight less than 1000g. The overall birth
rate is about 1 per 100 inhabitants per year [17].
Usually the premature infants’ retinas have underdeveloped vascularisation.
ROP is believed to affect the postnatal abnormal growth of these retinal blood
29
2. MOST PREDOMINANT EYE DISEASES
vessels, resulting in the formation of vascular shunts, retinal neovascularization,
and even tractional retinal detachment which in severe cases may lead to blind-
ness [17]. It is a disease which affects both eyes of the infants and in some cases
the effect may be irreversible and lead to blindness.
Figure 2.11 illustrates how the vasculature differs between the normal out-
growth and the patients with ROP [63].
Figure 2.11: Illustration of differences between normal and abnormal retinal blood-vessel development in the child with ROP.
ROP was first described by Terry in 1942-1943 as ”retrolental fibroplasia” [64,
65]. In the following 10 years, ROP was recognised as the largest cause of blindness
in developed countries and was growing in epidemic proportions.
Soon after, oxygen therapy 1 was recognised as the major cause of ROP and
hence the use of it was restricted [65]. As a result of this discovery, the incidence
of ROP decreased significantly. However, this adverse reaction was also associated
with an increase rate of morbidity and mortality in the premature infants [66, 67].
Therefore the oxygen therapy was once again brought in but supplemental oxygen
delivery to the premature infants was monitored carefully to main adequate blood
levels [68].
During 1980s and 1990s new treatment modalities such as vitamin E supple-
mentation, cryotherapy, laser photocoagulation and nursery light levels were stud-
ied and considered effective in reducing chances of occurrence of ROP [17, 65].
Even with the controlled oxygen level and the new treatments, the number of
1Oxygen therapy is the administration of oxygen for chronic or acute patient care.
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2. MOST PREDOMINANT EYE DISEASES
infants with ROP has increased since then [69, 70]. This is mostly due to the
advancement in technology and hence the increased survival rate of very low birth
weight infants [70, 71].
2.4.1 Worldwide Effect of ROP
Throughout the years ROP occurrence has remained very high and one of the
areas of interest and research. This may be due to the fact that this disease affects
premature infants and in some cases leaves long lasting irreversible results. In such
cases, the patient may be severely visual impaired or even blind. These patients
will have to go through life with a condition which could have been easily avoided
or minimised if they were treated on time.
Despite the available treatments and research being conducted in the field of
ophthalmology, ROP still is known to be one of the major causes of blindness
in children in both developed and developing world [70, 72]. The proportion of
childhood blindness caused by ROP goes from 8% in high income countries to
40% in middle income countries. In Australia and New Zealand, every 1 in 10
premature infants develop severe ROP [73].
Retinal detachment is quite uncommon in children, accounting for only about
1.7% and 5.7% of all retinal detachments [74], but it is the cause of blindness in
ROP. In general, retinal changes which may be indication of regressed ROP, include
myopic changes, displacement of macula and retinal vessels, retinal folds, pigmen-
tary changes, incompletely vascularized peripheral retina, abnormal branching and
tortuous and telangiectatic vessels [75].
2.4.2 Risk Factors of ROP
There are many risk factors associated with ROP. With advances in the neonatal
care, the number of surviving premature infants has increased significantly, which
in some case may lead to development of ROP.
The low birth weight and low gestational age are known to be strong risk factors
of ROP, where the smallest infants are more likely to develop ROP.
Oxygen has been recognized as another significant risk factor since the 1950s.
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2. MOST PREDOMINANT EYE DISEASES
However, the direct correlation of duration and concentration of oxygen with sever-
ity of ROP is not yet confirmed.
Other factors such as degree of illness, sepsis, blood transfusions, white race,
multiple births, and being born outside a hospital also increase the chances of
developing ROP [17, 65].
Socioeconomic factors and health care conditions of each country should be
considered while recognising the risks associated with ROP. Statistics have shown
that the occurrence of ROP is significantly increased in the developing countries
due to health care system and lifestyle in comparison to developed countries.
2.4.3 Classification of ROP
Once the patient is diagnosed to have ROP, to begin the treatment, the first step
is to classify the ROP. The studies have shown that more aggressive diseases are
located in the posterior section of the eye. Figure 2.12 represents the zones and
extent which are used to determine the classification of the ROP [17].
Figure 2.12: Classification of ROP for the left eyes [17]
The classification comprises of three parameters:
1. Location - zone of the disease in the retina [17]:
• Zone I is the posterior circle centred on the optic disc. Its radius is
about twice the distance from the disc to the centre of the macula. It
is defined as the most posterior location of disease.
• Zone II is a circle centred on the disc with a radius equal to the distance
to the nasal ora-serrate.
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2. MOST PREDOMINANT EYE DISEASES
• Zone III comprises the remaining temporal crescent.
2. Extent by clock hours of developing vasculature involved
3. Severity - stage of the observed abnormal vascular response [76]:
• Stage I - mild abnormal blood vessel growth.
– No treatment is required and the child eventually may develop nor-
mal vision without further progression.
• Stage II - moderate abnormal blood vessel growth.
– No treatment is required and the child eventually may develop nor-
mal vision without further progression.
• Stage III - Severe abnormal blood vessel growth.
– Abnormal blood vessels formation towards the centre of the eye
instead of following the normal growth pattern along the surface of
the retina.
– Some infants may not need treatment and develop normal vision.
– Some infants who have certain degree of Stage III and ”plus dis-
ease1” need treatment to avoid retinal detachment 2.
• Stage IV - Partial detachment of retina.
– Treatment is required. The bleeding caused by scars of the abnor-
mal blood vessels cause traction and pulls the retina away from the
wall of the eye.
• Stage V - Complete detachment of retina.
– Treatment is required. If the eye is not treated, the child may have
severe visual impairment and even blinded.
1Plus disease is when the blood vessels of the retina have become enlarged and twisted. Thisindicates the worsening of the disease. Treatment may prevent retinal detachment. Prior to theformation of plus disease, significant vasoconstriction may be present.
2Retinal detachment occurs as a result of accumulation of the Sub-retinal fluid in the spacebetween the neurosensory retina and the underlying retinal pigment epithelium. It is classifiedinto Rhegmatogenous, Tractional and Exudative based on the mechanism of the sub-retinal fluidaccumulation [77].
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2. MOST PREDOMINANT EYE DISEASES
2.4.4 Screening for ROP
The suggested examination time for the first visit is about 32-34 week of post
menstrual age and for the second visit is about 38-40 weeks post menstrual age [17].
Prior to screening the pupil of the eye needs to be dilated. To dilate the pupil
three times every 5-10 minutes eye drops are used.
At the time a nurse needs to be present to constrain the movement of the
infant and also look out for vital signs and clear airways, as Bradycardia due to
the oculocardiac reflex is a recognized to cause complication during the examina-
tion [17, 69].
During the screening process, follow-up and therapy the location, extent and
severity of disease are monitored and evaluated. The changes in the different
segment s of the eye, presence of persistent and dilated vessels in the retina are
monitored to see whether the treatment is needed [17].
Digital retinal wide-field imaging system is used to monitor and capture images
of the retina. Using the obtained data evaluation of the shape, degree of arbori-
sation, diameter of retinal vessels and estimate the severity of the disease even in
the absence of complete imaging has become feasible.
2.4.5 Treatment of ROP
Once the patient is diagnosed and is in need of treatment, photocoagulation ther-
apy or cryotherapy is recommended. Since the early 1990s, laser photocoagulation
has been used [78, 79, 80, 81] and is the preferred treatment method in comparison
to cryotherapy [17, 82, 83, 84].
Incidence of ROP have significantly reduced as a result of by better screening
and prophylactic cryotherapy or laser photocoagulation [75]. The treatments
have reduced the occurrence of blindness by approximately 25%; however, the
visual outcomes after treatment are often poor and patient may not have 20/20
vision.
The American Guidelines indicate the time to treatment has to be within 72
hours [17], but in some cases treatment should be provided without further delay.
These include patients with advance stage of the disease or those with zone I and
rapid progression disease.
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2. MOST PREDOMINANT EYE DISEASES
Recently it has been advised to start early treatments to avoid rare potentially
blinding disease. Earlier treatment is now recommended for aggressive forms of
ROP, such as zone I and posterior zone II disease. In these cases the treatment
can occur as early as 30.6 weeks post menstrual age [17].
2.4.6 Importance of Retinal Vasculature for Diagnosing
ROP
As indicated several occasions in section 2.4, ROP is an ophthalmological disease
which is caused due to abnormal growth of the vasculature in the retina. The extent
of the damage of this complication is dependent on the screening and diagnosis time
as early detection reduces the possibility of severe complications and blindness.
Hence an automated monitoring system could be used as an assistive tool to
aid the technicians and medical practitioners in their diagnosis. This system can
be used in remote, rural areas as a preliminary diagnostic tool which distinguishes
the patients prone to ROP from the normal patients. Moreover, by further analysis
of the retinal vessels in cases where severe cases of ROP are detected, the system
may outline the regions of the retina which are affected and are in need of further
treatments.
In order to create this system, it is crucial to extract the exact location of
the retinal vasculature. This can be achieved by analyzing the fundus retinal
images using image processing techniques. In this study, new automated image
analysis approaches have been considered for vascular localization and key feature
extraction. More details are included in the consecutive chapters.
2.5 Glaucoma
In Greece in 400 BC, the term Glaucoma was first used by Hippocrates to describe
a dimming of vision. Many years later, in 1862, the pharmacology of Glaucoma
was first detected with the isolation of physostigmine from the calabar bean [85].
Glaucoma is now the second leading cause of irreversible visual loss and blind-
ness [1]. Due to asymptotic characteristics of this disease [86] and with the aging
population and health issues such as diabetes [87], the incidences of Glaucoma
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2. MOST PREDOMINANT EYE DISEASES
remain high and an area of concern. Hence, to minimise vision loss in patients,
early detection and treatment of Glaucoma is essential.
It has been found that the cause of Glaucoma is the progressive loss of Retinal
Ganglion Cells and their axons.
This in turn causes morphological changes in the OD and visual field [35, 88,
89]. The initial signs observed are usually hemorrhage-associated retinal nerve fibre
layer defects. This is then followed by the visible changes of the OD, including the
thinning of the neuroretinal rim, pallor and progressive cupping of the OD. Often,
the visual field defects are detected at the later stages, where more than 40% of
axons are lost [35].
2.5.1 Worldwide Effect of Glaucoma
The leading cause of the irreversible blindness in the world is Glaucoma. It is also
the most common cause of blindness after Cataract. Worldwide, it has contributed
to the 14% of the blind population. Those accounts for about 70 million people,
from which 10% have been bilaterally 1 blinded by this disease [90].
2.5.2 Pathogenesis of Glaucoma
It is believed that Glaucoma damages the ganglion cell and its respective axons,
which comprise the Retinal Nerve Fiber Layer (RNFL) [90].
The progression of this damage results in asymmetric changes to the Optic
Cup (OC) and as a result visual field loss. Since there is no functional loss prior
to severe structural damage, up to 40% [35, 90], it is quite difficult to detect
Glaucoma early on in the disease progression.
The morphology of the defected RNFL appears to follow the normal structural
pattern of the retinal RNFL. The RNFL is usually striated. It radiates from the
OD and is thickest in the superior and inferior poles in comparison to the nasal
and the temporal poles. However, the Glaucomatous RNFL changes can present
as focal wedge-shaped defects of varying width radiating from the optic nerve head
or as diffuse loss of the striations in RNFL [90, 91]. Focal loss is often detected in
1Bilaterally means affecting both eyes
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2. MOST PREDOMINANT EYE DISEASES
the superior and inferior nerve fibers as Glaucoma usually affects these regions.
2.5.3 Risk Factors of Glaucoma
In the literature, several factors have been found which may influence and increase
the possibility of occurrence of Glaucoma [92]. These factors include:
• Age - older people are more likely to develop Glaucoma
• High Intraocular Pressure (IOP) - leading cause of Glaucoma
• Ethnicity - African, Latino and Asian descendants are more likely to have
Glaucoma
• Family History of Glaucoma
• Diabetes - the chance of Glaucoma doubles in diabetic patients
• Myopia (shortsightedness) - changes the internal structure of the eye, in-
creadint the chance for formation of Glaucoma
• Extremely high or low blood pressure - deprives the eye from adequate blood
flow, affecting the the rate of oxygen and nutrients as well as the waste
removal from the eye
• Long term usage of Steroid/Cortisone - increases the IOP and so results in
Glaucoma
• Injury to the eye
2.5.4 Classification and Screening of Glaucoma
Diagnosis and early treatment of Glaucoma is essential in prevention of vision loss.
Prior to implementing the right treatment method, the exact type of Glaucoma
has to be categorised.
There are several different types of Glaucoma. Some of which includes [90]:
1. Primary Open Angle Glaucoma - gradual increase in IOP
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2. MOST PREDOMINANT EYE DISEASES
2. Normal Tension Glaucoma - known as Low Tension Glaucoma and occurs
when there is a progressive damage to the optic nerve under normal IOP
3. Angle Closure Glaucoma - inherited
4. Acute Glaucoma - sudden increase in IOP
5. Pigmentary Glaucoma - type of an inherited Open Angle Glaucoma
6. Trauma related Glaucoma - acute or chronic development as a result of an
injury to the eye
7. Childhood Glaucoma - occurs in children when there is an abnormal increase
in the IOP
2.5.5 Treatment of Glaucoma
As mentioned earlier, Glaucoma may cause an irreversible blindness, therefore
early diagnosis and treatment of it could be crucial to manage this disease. De-
pending on the severity of the Glaucoma, several different treatment options are
available [90], including:
• Eye drops
• Medication
• Surgery - Traditional or Laser
2.5.6 Importance of Optic Disk and Macula for Diagnosing
Glaucoma
For years, clinical approaches were used for monitoring patients with Glaucoma.
The ophthalmologists considered OD and its variation in shape to monitor the
progression of this disease. However, due to limitations of the subjective nature
of the evaluation and progression of the disease, the use of computerised image
analysis technique is suggested.
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2. MOST PREDOMINANT EYE DISEASES
The automated assistive tool can aid in localising and extracting the infor-
mation from the OD and Macula. The obtained information can then assist in
diagnosis and prognosis of diseases such as Glaucoma. Further details in regards
to the image processing procedures involved in localisation of the OD and Macula
is covered in the consecutive chapters.
2.6 Summary
This chapter has covered the importance of vision in humans life. The field of
ophthalmology and some of the most common ophthalmological complications were
also discussed.
Three of the major leading causes of impairment in vision were investigated
in details, including the Cataract, ROP and the Glaucoma. The key features in
diagnosing these diseases have been defined and will be examined in more details
in the coming chapters.
The key features that have been found for Cataract, ROP and Glaucoma are
Iris and Pupil, Retinal Vessels, and OD and Macula respectively.
39
CHAPTER 3
IMAGE PROCESSING IN OPHTHALMOLOGY
Introduction Literature Review
Most Predominant Eye Diseases Image Processing in Ophthalmology
Thesis Outline Conclusion
Figure 3.1: Chapter Three Outline
3.1 Ophthalmological Complications
For years, diagnosing of ophthalmological disorders was being performed by obser-
vation. The results were very subjective and could have varied based on individuals
perspective and experience level.
In recent years, with advancements in biomedical applications and in particu-
lar image processing, new procedures have been implemented to provide a more
objective review of diseases and their diagnosis. To gain a better understanding
the current procedures, this reviews the available technology and image processing
methodologies.
Since the field of ophthalmology is quire broad and covers a wide range of infor-
mation, an in depth review of some of the main ophthalmological complications has
40
3. IMAGE PROCESSING IN OPHTHALMOLOGY
already been conducted and covered in the previous chapter. The diseases include
the Cataract, ROP and Glaucoma. The key features of interest in recognising
these diseases were also identified, including the Iris and Pupil, retinal vessels, OD
and Macula.
3.1.1 Importance of Image Processing in Ophthalmology
For years, health care system was only progressing based on the experiences and
knowledge of the health care professionals. However, with an increasing popula-
tion, longer life span and technological advancements, there is a need to change
the traditional methods of manual patient examination with more modern semi-
automated or automated procedures. This could be beneficial for both the patient
and the medical experts especially in regions where the number of experts are
much less than the number of patients.
Incorporation of the medical field with engineering, has led to a new field of
biomedical engineering. Biomedical engineering has played significant role in all
stages of medical procedure, including the prognosis, detection, treatment and post
treatment. This collaboration has led to increasing number of successful cases.
One of the main areas which has helped majority if not all the medical fields
significantly is imaging. With the advancements in imaging devices, nowadays
many of diseases and complications may be detected early on, leading to less
severe cases and early treatments.
Despite these significant life changing outputs, there is still much more to be
done and imaging continues to be a growing field.
Ophthalmology is also benefited significantly from imaging devices. Similar to
other medical fields, imaging has helped ophthalmologists in their prognosis of dis-
eases and their progression, detection of complications, inter-operative procedures,
post treatments and many more. It has also allowed researchers to have better
knowledge and view of the underlying structures of the eye, and its complications.
Imaging consists of different sections. The foremost step is the image capturing.
It is important to consider the requirements and the purpose of the image; based on
these specifications the image can then be obtained. Once the image is captured,
the image may be analyzed manually by the ophthalmologists. However, in many
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3. IMAGE PROCESSING IN OPHTHALMOLOGY
cases the analysis by an expert may not be possible.
With the increase in world population and limitations in the experts and ad-
vanced resources, the manual analysis of the captured images may not be possible;
as a result a new field of telemedicine is introduced. Telemedicine is especially in
use in growing and developing countries and remote locations of developed coun-
tries such as Australia. This is where the images and preliminary analysis is done
remotely and automatically.
This new field is very much dependent on the collaboration and close work of
medical professionals and biomedical engineers. The knowledge of the engineers in
image analysis and the experiences of the medics have allowed the image processing
to be achievable and of great importance even in the field of ophthalmology.
The flowchart 3.2 illustrates the steps undertaken in image processing.
Eye Image Acquisition Image Processing Interpretation Display
Figure 3.2: Stages undertaken in Image Processing
3.2 Image Processing Procedures
As mentioned thus far, image processing has been the key for much advancement
in both fields of ophthalmology. The basis for the validity of the knowledge and
understandings in these fields had become feasible due to applications of image
processing.
The name ”Image Processing” indicates a system or program which is capable
of manipulation of an image. Based on this, the first step to consider would be
the image which is the input to this system.
Once the image has been acquired, the next stage would be manipulation or
processing this image such that the required information could be obtained. This
can be achieved by applying a computer based procedure, a program, or an algo-
rithm to the image. The objective at this stage is to extract the region of interest
such that the required information could be obtained.
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3. IMAGE PROCESSING IN OPHTHALMOLOGY
The next stage is to interpret the findings which could be a visual interpretation
or a quantitative analysis of the results. The findings can then be displayed to the
user.
All the image processing applications follow these basic steps. However, de-
pending on the objective of the research and the required results, there might be
some minor changes to the steps.
In this study the main objective has been to detect the key features of the
eye, including the Iris, Pupil, retinal vessels, OD and Macula. To gain a better
understanding of the available literature and possible processes in biometrics and
ophthalmology, in this section, some of the image processing methodologies used
to detect these features have been reviewed and discussed.
3.2.1 Image Processing in Biometrics
Another area in which image processing plays a significant role, is Biometrics.
Biometrics refers to characteristics, physiological and behavioural, which may be
used to identify individuals.
There are several distinctive physiological characteristics, such as finger print,
DNA, facial recognition, Iris recognition and many more. The behavioural char-
acteristics include the locomotion, voice and other behaviours which may be used
to distinguish people.
For years, each of these characteristics has been investigated. Many studies
have looked into the advantages and disadvantages of each of these biometric
characteristics.
One of the most recent fields of interest in biometrics is the Iris recognition.
In recent years, the Iris recognition systems have become of significant interest, in
particular for security applications. Therefore, many studies have been performed,
aiming to identify this biometric characteristic. Image processing procedures have
been the main tool used for this characterisation.
This is due to the higher sensitivity, accuracy and automation capability of the
image processing tools. Its flexibility and robustness has been able to provide the
users with extensive amount of precise information.
The chosen area of interest in this study is the Iris. Therefore, it is important
43
3. IMAGE PROCESSING IN OPHTHALMOLOGY
to propose a methodology to detect and localise its boundaries with high precision.
Many approaches have been suggested, but majority assume that the Iris is circu-
larly shaped. Some of the current available procedures have been discussed in more
details in the image processing procedures for Iris recognition in Section 3.2.2.1.
3.2.2 Image Processing in Ophthalmology
For each ophthalmological feature of interest, many different approaches have been
investigated and studied in the literature. Some of the most widely techniques for
detection of the Iris and Pupil, retinal vessels, Optic Disk and Macula has been
reviewed and discussed in this chapter.
3.2.2.1 Iris and Pupil Localisation
Majority of the performed procedures can be separated into two groups, consid-
ering two different assumptions. The first group considered Iris and Pupil to be
circularly shaped and therefore the suggested procedures approximate the loca-
tion accordingly. The second group aimed to exactly localise the Iris and Pupil
boundaries.
Circular Assumption:
The two commonly used method of Iris and Pupil localisation are the Daugman
and Wildes.
For localisation Daugman proposed the use of Integral Differential Operator
which acts as circular edge detector, implemented on camera shot images. This
procedure was first introduced in 1994 for biometric applications with the false
acceptance probability of about 1 in 1031, accuracy of 98.6% and processing time
of about 7 seconds [93, 94].
Nishino continued on the work of Daugman by introducing an elliptical shape
assumption of the Iris and Pupil. This improved the procedure as gaze direction
was no longer of importance and non-forward looking images could have been
analysed [94, 95].
As mentioned, Daugman is a very well-known method which has been modified
by many over the years. One of the more recent modifications and extension of this
approach has been suggested by Ferreira et.al. [96]. The results from the use of
44
3. IMAGE PROCESSING IN OPHTHALMOLOGY
Template Matching in order to reduce the computational complexity and reflexion
removal have shown to be successful in faster detection of Iris and Pupil by about
7 to 10 times of the Daugman method, with the recognition rate of about 87.2% -
88% for different databases. However, since in the proposed procedure templates
have been used, the exact boundaries have not been extracted.
One of the widely used approaches for detection of features of interest of the eye
is Circular Hough Transform (CHT). It was initially introduced by Wildes, where
the edge detection and Hough Transform (HT) were applied in order to determine
the Iris boundary for biometric purposes [94, 97]. The results are of very high
accuracy, 99.9%, with the processing time of about 9 seconds. However, since CHT
is a three dimensional process, it is computationally complicated and requires large
storage spaces. Moreover, it requires pre-filtering and prior knowledge regarding
the location of the Iris or Pupil.
Cui et.al. localised the Iris and Pupil by following a coarse to fine strategy,
applying Canny edge detection first, followed by HT in order to increase the speed
of detection [94, 98]. The proposed algorithm detected the boundaries of the
Pupil using the low frequency of the simplest Wavelet Transform, Haar Wavelet
Transform, and Iris with an Integral Differential Operator. This robust process
has a high accuracy of 99.54% and the processing time of about 0.2 second. The
precision of detection is less than those proposed by Daugman and Wildes but the
processing time is improved rapidly. The downfall of this process is that it only
estimated the boundaries of the Iris and Pupil.
In another study performed by Daouk et.al. [99], similar steps are undertaken
while considering the Iris localization, pattern extraction and matching. The au-
thors have proposed the fusion of Canny edge detection and CHT for detection of
the Iris. The Haar wavelet transform is applied in this in order to extract the Iris
patterns. The pattern matching is performed and quantized using the Hamming
Distance Operator. The success rate is 93% with the average computational time
of 31 seconds. The concept used by this group can be beneficial for the biomet-
ric identification systems. However, due to long processing time, it may not be
feasible to be used in ophthalmological applications.
A more recent modified version of the Wildes approach using gradient of the
image, has been implemented by Moravik et.al. [100]. The gradient is found using
45
3. IMAGE PROCESSING IN OPHTHALMOLOGY
the first derivative of the image, followed by CHT. The obtained results indicate
that due to loss of image resolution; the circle configuration in many cases will
be lost, resulting in misdetection and reduction of accuracy in comparison to the
CHT process.
Another approach suggested for Iris and Pupil detection is using a Wavelet
Analysis to base a Minimum Variance method in order to detect the Pupil bound-
ary and the brightness gradient method to detect the Iris boundary. This approach
has been suggested by Shen et.al. [94, 101]. The Minimum Variance method in-
creases the speed of detection while the gradient method enhances the precision
via restricting the search area. The processing time is faster and the results are
better than the CHT method.
The use of Fisher Linear Discriminant Analysis (FLDA) method and Principal
Component Analysis (PCA) method has been suggested by Haq et.al. for Iris and
Pupil localisation and normalisation for biometrics applications. The first step is
to use a threshold value to extract the darkest region, defined as a Pupil. The
calculated radius and center is then used to draw the Pupil boundary. Another
threshold value is then applied to the image in order to darken the areas associated
with the Iris and Pupil of the eye. Then the medial filter is applied multiple times
to the complement of this image. Using the Sobel edge filter and estimated radius
size of the Iris, the outer boundary of the Iris is then approximated. The features
are extracted using the FLDA and PCA. The suggested technique has a recognition
rate of 97% and has been suggested to be suitable for real time applications [102].
However, the methodology is an approximation of the boundaries and depending
on the chosen thresholds used in the process, it may or may not be suitable for all
images.
Non-circular localisation of the Pupil has also been studied by Basit et.al. In
this study, initially a point inside the Pupil is detected by using the decimation
algorithm. Once the point is determined, the centre of the Pupil is calculated using
its centroid. This is followed by determining its radius using the binary image of
the region. The exact pupil boundary was then discriminated by joining a sequence
points selected with maximum rate of change. The Iris boundary was circularly
approximated in this study using the intensity gradient in radial direction. The
results show high accuracy of up to 99.86% and 99.3% for Pupil boundary detection
46
3. IMAGE PROCESSING IN OPHTHALMOLOGY
and 99.6% and 99.21% for Iris boundary detection [94].
Using the least significant bit plane the Iris pattern has been extracted by
Bonney et.al. [103]. In this case, the Pupil boundary has been detected by applying
the binary morphology to the bit plane. The least significant bit place, bit-plane
0, is used to determine the Pupil as it is quite homogenous and identifiable. For
the Iris boundary, the standard deviation along the vertical and horizontal axis
of the image intensity plot has been determined. Using the thresholded results,
multiple attempts are made in order to match the deviation vectors with the actual
Iris boundaries. About 10%-25% of the outcomes showed poor localisation. The
advantage of this technique over the other available processes is that there are
no requirements of the frontal view and it may be implemented on the off-angle
images.
Another study which considered the Pupil boundary detection was conducted
by Mehrabian et.al. [104]. The suggested Graph Cuts procedure uses the gray
level pixel values to determine the weights of the links to the graph. In this case
the Pupil is considered as the region of interest, while the remaining areas of the
image are considered as the background. The advantage of this technique over
the majority of the other approaches is that it may be used for off view angles
images. Although the procedure has high detection precision, the downfall of this
procedure is that a circular boundary has been used to outline the Pupil.
Intensity gradient values can also be used to detect the position of the Iris and
Pupil. Basit et.al. [105] have used this approach. In this case, the inner boundary
of the Iris is locates via determining the center and radius of Pupil. Firstly, moving
Average filter is used to determine a point inside of the Pupil. For binarization,
the maximum threshold value of the histogram is used. Finding the centroid of
the binary image defines the center of the Pupil. The average number of non-
zero pixels in any direction can then be used as the radius of the Pupil. Using
the center and radius, the Pupil boundary is then plotted. To determine the Iris
boundary, the image is first filtered using the Gaussian filter. To circle bands
are set, defining the region of interest such that the Iris boundary is in between
them. By determining points with the maximum gradients in this region, the Iris
boundary is then set. The proposed results are of very high accuracy of about 99%
for Iris localisation, with the processing time of about 0.3-0.4 second. However,
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3. IMAGE PROCESSING IN OPHTHALMOLOGY
the obtained boundaries are assumed to have a circular shape and therefore cannot
be used for exact boundary detection.
Non-circular Assumption:
The use of pointwise level set algorithm has been suggested for precise localisa-
tion of the Iris. This method uses the stepwise deformation of the initial contour.
Using horizontal and vertical histogram of the image, the center of the Iris is cal-
culated ad is set as the initial point of the contour and its tracing. The iterative
algorithm of the contours starts from the Iris center going outwards. As long as
the variations between the gray levels are small it will continue, but as soon as
the variation becomes significant it stops. The number of iterations used for each
image in this study was about 25 with the processing time of about 2 seconds.
On average this process is quite robust, with success rate of greater than 95%.
There are no constraints in detection and therefore can be used to detect the Iris
in presence of eyelids and eyelashes. The main disadvantage of this process is that
it is sensitive to the rotation of the fundus image [106].
For detecting the Iris and Pupil, Masek et.al. proposed the use of CHT [107].
Firstly the edges have been detected using Canny edge detection. The gradients
from both vertical and horizontal direction have been determined, weighted and
used as thresholds for recognition of boundaries. By manually setting an approxi-
mate range for radii of the Iris and Pupil, then the boundaries have been detected
by applying Hough Transform. The accuracy of detection in this case was about
83%. This technique is quite robust. However, further improvements on the pro-
cess are needed, such as automating the estimation of the radii of Pupil and Iris
as well as improving the accuracy.
In order to localise the Pupil, Ritter et al. [108], suggested the use of active
contour models. In this case, the internal (desired characteristics) and external
(image characteristics) forces have been set and moved across the image, until
equilibrium has been reached creating the contour and locating the boundary of
the Pupil. For this case, the internal forces have to form an expanding discrete
circle from the center of Pupil, while the external force have been found using
variance of the image. Localisation of the boundary has been successful but further
refinement and calculations of the results were needed.
As it can be seen, majority of the proposed techniques in the literature have
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3. IMAGE PROCESSING IN OPHTHALMOLOGY
assumed Iris and Pupil to be circularly shaped and localised the regions accord-
ingly. As part of this study, the author has looked into exact localisation of the
Iris and Pupil boundary. More details are provided in Chapter 6.
3.2.2.2 Retinal Vessel Detection
Localisation of the retinal vasculature has been one of the main areas of interest in
ophthalmology. This is mainly due to the fact that many of the ophthalmological
disorders affect the retinal vessels and its shape, such as the case of ROP which
was mentioned in Chapter 2. In this section, some of these approaches have been
discussed in more details, including the use of different filters, Matched Filters and
Wavelet Analysis techniques.
A study performed by Chanwimaluan et.al. [109] has used the Matched Filters
(MF) to enhance the blood vessels by detecting piecewise linear segments. For
each image, more than twelve 16 by 15 pixel kernels are convoluted to the image
in order to maximise the response. This has then been followed by a local Entropy
based thresholding procedure to detect and segment the spatial structure of the
vessels using the co-occurrence matrix. Lastly the Length filtering has been imple-
mented to remove the misplaced pixels and connect the discontinued vessels. The
detection has been successful; however there is still need for further improvements
for robustness of the procedure as well as the removal of additional lesions which
cause misdetection in the results.
Rahebi et.al. [110] has proposed the use of Gabor filter on a Threshold MF
images in order to classify each pixel as a vessel and non-vessel. The authors
applied a threshold value to the MF response of images and then adjusted the
threshold using the response from the Gabor filter which is a Gaussian kernel
shaded by a sinusoidal sheet wave. The obtained results had a reasonable accuracy
of about 93.80-94.82%.
One has suggested the use of zero mean GMF with First Order Derivative
Gaussian (FDOG), proposing a method called MF-FDOG. The vessels are first
detected using threshold response from the GMF. The results are then modified
based on the response of the FDOG. This approach has improved the process-
ing time by reducing the computational complexity. However, the accuracy of
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3. IMAGE PROCESSING IN OPHTHALMOLOGY
the results is about 93.82% with 10 seconds processing time. The accuracy and
sensitivity of the process may further improve by further investigations in noise
removal [111].
Three different Template Matching Algorithms have been used to compare the
detection of vessels in the study performed by Banumathi et.al. [112]. The Gaus-
sian Matched Filter (GMF), Binary Matched Filter (BMF) and Kirsch Template
Matched Filter (KMF) have been applied. For each case about 8-12 minimum
number of templates were used. For processing the GMF and KMF required more
memory. The results indicate that BMF provided better results for detection of
small vessels and capillaries, while KMF process could have been performed to
both gray level and coloured images. The processing time of the GMF, BMF and
KMF were 20 minutes, 4 minutes and 1.5 minutes respectively. Furthermore, in
the GMF and BMF processes, more noise was removed in comparison to the KMF
process. Since the processing time is quite long and the obtained results are quite
noisy, it can be said that the MF approach may not be a suitable process for fast
vasculature detection.
Canny filters have been used to detect the edges of the vessels in the study
performed by Fiorin et.al. [113]. In this study, the centreline of the vessels has
been manually selected. After image enhancement with the use of Canny filters
the vessels edges were extracted and vessels tortuosity calculated. To reduce the
processing time, pixels which belong to the vessels or their neighbouring vessels
were detected in order to decrease the region of interest. Despite the success in
calculating the tortuosity of the vessels, since this process was semi-automatic
and the location of vessels were somewhat selected at the beginning, the obtained
results cannot be compared with the other techniques.
Another simple, fast and sensitive algorithm in the literature has been sug-
gested by Zhang et.al. [114]. The authors have proposed the use of directional
local contrast as the vessel detection feature. In this case, the blood vessel shape
kernel is set as a vessel. Each pixel is analysed using the Weber contrast mea-
sure in all directions. The pixel is then defined as a vessel of background. This
process is continued for small vessels adjusting the parameters of the blood vessel
shape kernel. The processing time is about 5-7 seconds, with the true positive
rate of about 82%. Despite the fact that this procedure has high sensitivity, it is
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3. IMAGE PROCESSING IN OPHTHALMOLOGY
not robust and would require high quality images which may not be feasible in
developing regions or rural areas.
An automated process for vasculature detection has been proposed by Siddalin-
gaswamy et.al. [115]. In the proposed hybrid methodology, the blood vessels have
been enhanced while the background has been supressed using Gabor filter. The
vessels have then been segmented using the Entropic Thresholding. The obtained
results appear to have successfully located the retinal vessels with the sensitiv-
ity of 79%-91% and specificity of 94%-98% with a processing time of 20 seconds.
This suggests that the results are promising, however in comparison to the other
techniques the process may further improve by reduction in processing time and
increase in accuracy.
The use of Wavelets has been another approach in detection of blood vessels.
Bankhead et.al [116] have used the Isotropic Undecimated Wavelet Transform
(IUWT) to outline the blood vessels. Coefficients less than the set threshold of
20% for each wavelet level are set as vessels. Based on grain sized, the noises are
removed. To improve the precision of detection, a thinning algorithm is used. The
image profiles have then been perpendicularly computed across the spline fit of
each of the detected vessels centerlines. The accuracy of this procedure has been
about 93.71% with a processing time of 9-25 seconds. A limitation of this study
is that with decrease in contrast, the appropriate crossing may not be found in
order to detect the edges of the vessels and therefore affecting the accuracy of
the procedure. The use of interpolation was suggested but no further results were
included in this case.
In another study performed by Selvath et.al., the Curvelet Transform (CT) has
been used as an efficient edge detection methodology. Using the combination of
the CT and Support Vector Mechanism (SVM), the pixels have been segmented
as vessels and non-vessels. The authors have enhanced the retinal images using
the CT which is more efficient than the Wavelet Transform. CT occurs when the
Fourier space is divided into concentric circle and then into wedges which captures
the structural activity. Once the images are enhanced, the features are extracted
and segmented using the SVM and Radial Basis Function kernel. It is a pixel based
classification technique which finds the hyper-planes with maximum separation
between the decision function vector and the support vector. The results indicate
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3. IMAGE PROCESSING IN OPHTHALMOLOGY
better segmentation with enhancement of images. However, the accuracy of this
procedure is not quite high and varies between 78.59%-91.13%. For more accurate
vascular localisation this method may not be sufficient and so further investigation
has to be performed [117].
Combining the Adaptive Local Thresholding (ALT), CT and SVM concept has
been the basis of another study conducted by Xu et.al. [118]. The ALT has been
applied to produce a binary image which has then been used to normalise the
image. The grain sizes less than 100 in this case are considered as noise and are
removed. This process has outlined the location of large vessels. Similar to the
previous technique, the smaller vessels where then segmented by CT and classified
by SVM. In this case the computational complexity is reduced. The accuracy
of this procedure is about 93.2% and sensitivity of 77.60%. The process has a
reasonable sensitivity, however further improvements is needed to improve the
accuracy especially in the pre-processing stage as it had inflated the width of the
large vessels.
From the above literature discussing the retinal vasculature localisation, it can
be concluded that majority of the approaches consider edge detection procedures.
Therefore, in order to help in diagnosis and treatment of diseases such as ROP,
in Chapter 7, multiple commonly used edge detection filters have been examined
and compared for vessel localisation.
3.2.2.3 Optic Disck and Macula Localisation
As mentioned in Chapter 2, other main key features of retinal images which may
be used for disease diagnosis are OD and Macula.
Optic Disk:
Over the years, there have been many different approaches and investigations,
aiming to locate the OD and extract its features. Some of them have been discussed
in more details in this section.
Localisation of OD Center:
The use of Histogram Matching for center localisation of the OD has been sug-
gested by Dehghani et.al. [119]. The authors of this paper have found the average
histogram of each colour component of the image, in order to create a template
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3. IMAGE PROCESSING IN OPHTHALMOLOGY
for locating the OD center. The obtained results display high accuracy for center
localisation of the OD, 91.36%-10% for different databases. The processing time
for a single image is about 27.6-32.5 seconds. The objective of this study was to
locate the OD center and use that in the future studies for locating the OD bound-
ary. Therefore, further investigation is still needed to detect the OD boundary and
improve the accuracy of the results due to high number of misdetections.
Sekhar et al. [120] used clustering and histogram technique to detect the geo-
metric shape of the OD. In the initial step, the brightest pixels in the image have
been clustered together. Three windows for pixel selection have been formed, max-
imum difference, maximum variance and highest gray level value after Gaussian
low pass filtering. The histograms for the images associated with these windows
were then found. Using that, the location of the OD has been defined by selecting
the image with the largest number of brightest pixels. The OD localisation using
this technique has an accuracy of 99.5% and the best results were obtained for
images without dilation. This methodology only looked into approximating the
location of the OD and did not define its boundary.
Youssif et al. [121] proposed the use of directional pattern of the retinal blood
vessel to localise the OD center. The retinal region of the images were masked and
adjusted for illumination and intensity. Vessels direction map was then obtained
using a simple two dimensional GMF. With the aid of resized GMF, the difference
between the vessels direction and the MF was measured. The estimated centre
was then determined, as the point of the minimum difference. The OD center
detection using this technique had a very high accuracy of about 98.77%-100% for
different databases. However this technique is quite time consuming, taking about
3.5 minutes to detect the center. Moreover, using this technique only the center
of the OD is found and its boundary was not defined.
OD Boundary Using Vessels Location:
An automatic OD localisation approach, suggested by Mendoca et.al. has
combined the vascular and the intensity information [122]. By choosing the high
intensity regions in the calculated entropy of the obtained vascular directions, the
OD and its center have been localised. The method is of very high accuracy as
the obtained results indicate between 98.8-100% success rates. Despite the high
precision, since the vessels are localised first this method can take up to 90 seconds
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3. IMAGE PROCESSING IN OPHTHALMOLOGY
to process a single image. Therefore, it may not be of interest in cases where fast
processing time is essential.
Two different methods were suggested by Zhu and Rangayyan et al. [123, 124].
The first one is to use the Sobel filter and CHT for localising the OD bound-
ary [123]. Using Sobel filters the edges have been found in the images. A threshold
value was used to normalise the image. CHT has been applied to detect the cir-
cular region of the OD and approximate its center and radius. The process had a
success rate of about 90%-95% which suggests the need for further improvement.
Moreover, since the edge detection was the preliminary process, other filters may
also be investigated in order to improve the accuracy of detection.
The second study considered the use of Gabor filters and phase portrait analysis
to detect peaks in the node map [124]. Similar to the previous method the edges
have been detected using filters, however in this case the Gabor filter was used.
Using phase portrait analysis and intensity based condition; the peaks in the node
map have been checked and selected is they were part of the OD.and used to define
the OD boundary. The accuracy of detection in this case was about 88.9%-100
Welfer et.al. [125] proposed the use of a mathematical Morphology Model for
detecting the vascular structure. Using this, several marker points are chosen
and by implementing the watershed transform, OD boundary is detected. The
success rate of this study was about 97.75%-100%. However, this model was also
computationally complex and time consuming.
Direct OD Boundary Detection:
The use of Hybrid Level Set Model and Template Matching has been suggested
by Yu et.al. [126] in order to obtain regional information and local edge vector
for localisation of the OD. The OD size is initially estimated and the image is
normalised. With the use of TM and Directional MF, the OD is then localised.
To segment the OD, the location of the OD and its estimated size is the used in
the red channel of the original image to check the saturation level. Using this, the
blood vessels are then removed. The Hybrid Level Set Model and the Least Square
Ellipse Fitting is used to detect the segment the OD. The overall processing time
of the proposed technique is about 6.6 seconds, with a very high success rate of
99% for OD localisation. Using this technique, errors in detection may occur in
cases where brighter regions such as advance retinopathy are present in the image.
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3. IMAGE PROCESSING IN OPHTHALMOLOGY
The accuracy detection may also be reduced in special cases where the OD size
varies to the estimated size at the initial step of processing.
Joshi et al. [127] enhanced the previous work conducted by Chan-Vese [128],
using region-based active contour model to segment the OD, while incorporating
information from multiple image feature channels.
Esmaeili et al. [129] proposed the use of Digital Curvelet Transform and thresh-
olding on retinal images to estimate the location of OD. As a secondary analysis
for smaller sized ODs, Canny edge detector is suggested for locating the disc. This
method is computationally complicated.
Macula:
In comparison to the Iris and Pupil, retinal vessels and OD, not many studies
in the literature have considered or localised the Macula. A few of the proposed
procedures from the literature have been discussed further in this section.
Superpixel-based approach has been used by Wong et.al. to locate the center of
the Macula [130]. Using the suggested approach, the Maculas center was detected
with the average error of 30pixels.
Another approach has been to locate Macula based on the distance and position
of the OD [131]. It has been suggested that the Macula is located about 2 disc
diameter (DD) temporal to the OD. The mean angle to the horizontal between the
Macula and the center of the OD varies between -2.3 to -8.9 degrees. Using this
information, the location of the OD has been approximated with a sensitivity of
about 96.6%. Since the retinal features of each individual person varies to another
person, and more over the specifications of each image varies to the next, this
method may not be robust enough to accurately locate Macula for wide range of
images. It also does not work in cases where the OD has not been localised and
Macula is the only feature of interest.
Considering the literature and the importance of OD and Macula localisation
for disease diagnosis, this study considers other approaches for detecting these
features. In-depth discussion of the proposed techniques and the associated results
are discussed in Chapter 8.
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3. IMAGE PROCESSING IN OPHTHALMOLOGY
3.3 Study Design Considerations
Thus far, the literature on image processing techniques for detection of different
ophthalmological features has been discussed. In order to achieve the objectives
of this research, meaning revising image processing procedures and introducing
new techniques to assist medical practitioners in their diagnosis, it is important
to consider several factors covered in this section, as part of the study design
consideration.
To design any system it is important to have a realistic vision of its possibilities
and restrictions. For the case of an automatic diagnostic tool for ophthalmology,
several variables which have been considered are mainly influenced by the location
of the use and the experience of the medical practitioner. These factors include:
• Device, its specifications and functionality
• Person who is capturing the images, whether it is an ophthalmologist or a
trained technician
• Number of images being captured
• Image specification and the conditions under which the images are being
captured under
• Patients collaboration
The above points are only some of the factors which may have influenced
the captured images and therefore have had a direct impact on the final results
obtained from the image processing procedures in this case.
3.3.1 Examination versus Screening
As part of the design consideration, one has to decide on the purpose of the
intended function of the device. Medical devices have been used in all aspects
of ophthalmology. Two aspects are usually considered for automated diagnostic
tools. The first one is to use the device for examining the possible patients and the
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3. IMAGE PROCESSING IN OPHTHALMOLOGY
other is to screen and refer the patients to the medical experts for further diagnosis
and treatment.
For a complete diagnostic tool for screening the patients, a significant amount
of information and experts input is required. Additionally, many other unexpected
factors or complications such as rare diseases may also influence this system by
making it more difficult to achieve.
Since in this study, a broad range of features have been considered, the best
function for this automatic diagnostic tool would be to examine the patients and
provide the information to the ophthalmologists for further screening, diagnosis
and treatment.
Figure 3.3 illustrates the overall view of the proposed assistive diagnostic tool.
As it can be seen in the figure, the technician uses the fundus camera to capture
the retinal images from the patients. The suggested image processing procedures
and modifications in this thesis can be then implemented and performed as part
of the telemedical tool, on site in the preliminary image processing section. The
data can then be transferred directly or online for further examination. The final
results can then be provided to the ophthalmologist so that the diagnosis could be
made.
Figure 3.3: Suggested Image Processing stages.
While considering the ethical aspects, the stored data from this process can also
be used for future studies, monitoring the patients and creating a large database for
disease diagnosis. This can be the first step towards a single automatic diagnostic
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3. IMAGE PROCESSING IN OPHTHALMOLOGY
tool for all ophthalmological diseases.
3.3.2 Cost Effectiveness
The need for automatic diagnostic tool has become apparent with an increased
number of patients but limited number of medical experts. Additionally, objective
analysis of diseases and complications are now of more interest.
Despite the great need and interest in this system, one of the major drawbacks
of this is its associated costs. The directions of recent studies have been to reduce
these costs. Majority of the current technologies still rely on advanced image cap-
turing devices in their diagnostic tools. These devices are expensive and acquiring
it in remote locations are very difficult.
To avoid this issue, in this study, the basic retinal images obtained from fundus
cameras have been used for further processing. This technology is available in
majority of countries and does not require any further purchases.
3.3.3 Image Quality
As mentioned in the design consideration section, there are several factors which
influence the quality of images. Image capturing is the initial and main step in
any image processing procedure, as it directly affects the whole process.
High quality images can result in more accurate and clearer feature detection.
Once the features have been localised, the extraction of the information can also
be more easily performed. The precision of detection is greatly improved by high
quality images. However, high quality images need more storage facilities, which
may not be feasible or justified in many occasions.
For example, large numbers of patients are usually monitored in rural areas and
so if all the data has to be stored and backed up, large quantity of storage facilities
would be needed, which in turn could result in higher expenses and longer process-
ing times. Some might suggest the use of advancements in wireless transmission
of the information to another location. This may be feasible in developed nations,
but in remote locations and the developing regions, the costs of the equipment
are high while the accessibility to them is limited. As a result, the best way to
compensate for this problem is using images with a reasonable quality.
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3. IMAGE PROCESSING IN OPHTHALMOLOGY
Additionally, the diagnostic tool should be set such that at any given instance,
large quantities of images could be inputted, automatically detected and their
results stored for further review and examination of the patients.
3.3.4 Accuracy
Accuracy comes at a price. Majority of the performed procedures in the literature
can locate the Region of Interest (ROI) with very high precision. However, the
downfall is that they are computationally complicated and very time consuming.
Consequently, it is important to consider the extent for which this accuracy is
needed when it comes to designing an automated diagnostic tool. The required
precision is relevant to the image processing procedures, equipment and resources,
image quality and patient examination time. For example, in the initial examina-
tion where all the key features of the retina are to be considered and examined,
the processing time for each feature has to be shorter, which reduces the accuracy
of the detection. This might not be the case for the consecutive examinations,
where a certain feature at risk is critically examined.
Considering the limitations, the accuracy needed for detection varies. In this
study simpler image processing approaches have been proposed so that they are
less computationally difficult, with faster processing time.
3.3.5 Reliability
All the systems have to be reliable, meaning that they should perform and function
to the expected level at all times. Since the information and the results provided by
the assistive tool is the basis of the ophthalmologists’ judgements, it is important
for the system to be reliable.
The automated diagnostic tool should also be reliable in a sense that it would
perform in a similar manner under the same conditions. For a highly reliable
system, the overall result should not alter for similar specifications and conditions.
It should also be compatible with other available devices, suggesting that given
another input image using a different capturing device, the automatic assistive
tool would be able to provide a reasonable response with minor variations to the
accuracy of the final outcome.
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3. IMAGE PROCESSING IN OPHTHALMOLOGY
For the case of this research, the suggested and implemented procedures have
been chosen such that they provide a reasonable and reliable result for different
images and specifications.
3.3.6 Safety
One of the key factors considered when designing a medical device is safety. Pa-
tients and users safety are substantially important in this field.
Majority of the population would prefer procedures which are non-invasive.
The reason being is that there would minimal to no side effects and the recovery
time would be quite fast as well.
In the case of the automatic diagnostic tool, no direct treatment on the patient
is being performed. Therefore, there are no safety concerns in that regard. How-
ever, this system would be using images to perform its tasks. Some of the available
capturing technology may have some side effects which might cause concern for
the patients.
All the diagnosis and further treatment has to be suggested by expert medical
practitioners in this case. Therefore, the suggestive assistive tool is very safe.
3.4 Summary
In the continuous of Chapter 2, this chapter reviewed different image processing
approaches for detection of the key features of interest of the eye. The main
features of interest included the Iris, Pupil, retinal vessels, OD and Macula. Several
current available procedures for detection of these features were considered and
investigated. The advantages and disadvantages of each were studied and outlined
in this chapter.
Several study design considerations were also considered, including the impor-
tance of the study, its cost effectiveness, image quality, and accuracy of detection,
reliability and safety of the suggested procedures.
Furthermore, the general steps in image processing were also discussed as part
of Figure 3.2. The remaining chapters of this thesis use this outline for further
investigation and improvements to the available methodologies.
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CHAPTER 4
IMAGE ACQUISITION AND FUNDUS MAPPING
Introduction Literature Review Thesis Outline Conclusion
Image Acquisition Image Pre-Processing
Image
Fundus Mapping Refraction Study
Feature Localisation Feature Extraction
Figure 4.1: Chapter Four Outline of Image Processing Stages
4.1 Overview
Improvements in the field of ophthalmology are indebted to advancements in im-
age capturing procedures and instrumentation. Previously, the visual inspection
of the eye was the only source for disease detection and treatment. However, en-
hancements in imaging and its processing significantly changed these traditional
approaches.
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4. IMAGE ACQUISITION AND FUNDUS MAPPING
Implementing image processing procedures would require images and data.
The data is obtained through the ophthalmological image capturing devices. Each
device would have certain capabilities and restrictions, resulting in variety of dif-
ferent outputs. Therefore, depending on the disease of interest, the availability of
resources and technical knowledge, the required device could vary. Detail under-
standing of the disease and its characteristics can aid in choosing the device which
can provide the expected outcomes.
Moreover, majority of the times the raw data would not be sufficient for prog-
nosis of the disease. As a result further processing would be required at this stage
including the image mapping and the analysis of the associated errors in captured
images in comparison to the actual feature.
One of the processes which may be implemented to enhance the Field Of View
(FOV) of the results is the retinal fundus mapping. Fundus mapping is when
using a single instrument, multiple images are obtained and combined to create
a wider view range, which in turn could be used for better diagnosis of diseases.
As parts of this chapter, an improvement to the readily available fundus mapping
techniques is introduced.
Furthermore, it was noted that in majority of retinal images captured, the
study of light and its refractions through different matters have been ignored.
Although the variation in the light refraction between each matter is very small
but it is crucial to be investigated for each case, in order to determine the accuracy
of the captured image, disease diagnosis and also treatment. This is due to the
fact that each section of the human eye has its own refractive index and so this
affects how the light passing through these regions would bend. These indices
would also vary between individuals and the results can be obtained in the initial
patient monitoring. Using this initial information, the variation between the actual
location of the region of the eye and the image can then be defined.
In this chapter, the preliminary stage of image acquisition, fundus mapping and
studying the errors associated with images using the light refraction are considered.
The requirements for image capturing and preparation for further manipulation is
considered such that the optimum information could be extracted from the data.
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4. IMAGE ACQUISITION AND FUNDUS MAPPING
4.2 Image Acquisition
The foremost step in image processing is image acquisition. The intended purpose
of this step is to obtain the best possible data required by the specification of the
study. Therefore, prior to image capturing it is essential to investigate the overall
objective of the project and the desired specifications.
The objective of this study has been to introduce new image processing ap-
proaches on a readily available data sources. However, in order to distinguish and
characterise the findings it is important to have a sound knowledge about all the
stages of image processing. Therefore, in this section, the factors which have been
considered for acquiring a capturing device are discussed in more details.
There are few factors to consider prior to image capturing. The main factors
would be the available resources, funding and specialisation. Depending on the
disease characterisation, image requirements can be set, one of which could be
specifying the wavelength of interest from the electromagnetic spectrum.
In majority of cases where high precision is of interest, it is essential to ob-
tain images which would be of the highest possible quality, with good resolution.
However, in such cases the processing time of further stages of feature detection
and analysis may take longer. Higher resolution of images means more data to
analyse, hence slower processing time. As a result it is also important to consider
the processing time.
Other factors which may be considered prior to choosing the device [132] for
imaging include:
• Devices of interest
• Required resolution
• Speed of capturing
• Field of View (FOV) of the device
• Required lighting and ambient light
• Hardware processors
• Image Processing capabilities
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4. IMAGE ACQUISITION AND FUNDUS MAPPING
• Desired software package for image processing
It is also important to be aware of the device capabilities and restrictions so
that the best data maybe captured. Once the device is chosen, the images may
then be captured.
If the obtained data is in the form of video recording, certain frames maybe
selected as images with the use of a frame grabber. The frame grabber device
can capture, store and transmit images via interfacing and synchronising with a
camera [133]. The obtained images are then used for further analysis and manip-
ulations.
Figure 4.2, demonstrates a desktop setting for image capturing. The capturing
device is used to take recordings of the required object. The information is trans-
ferred to the hardware processor. Depending on the requirements of the study and
the set specifications of the device, the hardware processor can perform preliminary
modification and storage of data, which can then be transferred or transmitted to
the image processor. Majority of the image processing occurs at this point and
the results can then be displayed on the monitor.
Figure 4.2: Image capturing set up
In this study, the most important factor is obtaining fast image processing
response, post capturing of the data and irrespective of the device specifications.
However, the processing procedure should be compatible and implementable to
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4. IMAGE ACQUISITION AND FUNDUS MAPPING
any given image. To check the validity of this assumption, open source data are
used instead of collecting high resolution images.
4.2.1 Filters
To enhance the images, they are usually filtered. This filtration may occur using
the capturing device (Hardware filtering) or after the image has been obtained
(Software filtering).
4.2.1.1 Hardware Filtering
Usually, there are noises associated with image capturing. This is mainly due to
the slight movements of the patient and specifically their eyes. To reduce this
noise, hardware processor implements hardware filters.
Moreover, depending on the desired bandwidth of interest in the electromag-
netic spectrum certain wavelengths may be required to be removed which is achiev-
able via implementing hardware filters. An example of which is shown in Fig-
ure 4.3.
Another common hardware filtering is the illumination compensations. Since
the obtained images are not captured in an ideal environment, the ambient lighting
may affect the images and change the contrast. To minimise this affect, the lighting
may need to adapt accordingly. Therefore it is essential to consider and study the
light source prior to image capturing.
Figure 4.3: Capturing device, (1) Camera, (2) Lighting, (3) Hardware light filter
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4. IMAGE ACQUISITION AND FUNDUS MAPPING
Some of the factors which have to be considered while choosing a light source [132]
include:
• Type of the light source
• Intensity of the light source
• Direction of the light
• Angle of the light
• Location of the light source
• Distance from the object
• Surface of the eye
• Structure of the eye
Once the factors have been taken into the consideration the selected image
capturing device maybe used for acquiring the images.
4.2.1.2 Software Filtering
Once the images have been acquired, further filtering may be used to minimise
noises. In the literature, many different filtering techniques have been introduced
and investigated, each having specific advantages and disadvantages in comparison
to others. In upcoming Section 5.4, a few of such methodologies have been selected
and investigated further .
4.2.2 Image Databases
Once the device is chosen it may then be used to capture the desired images.
The objective of this study has been to investigate different image processing
approaches in identifying key features of the eye in order to aid ophthalmologists
in the prognosis process of diseases. Therefore the desired images have been taken
from the human eye and its retina.
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4. IMAGE ACQUISITION AND FUNDUS MAPPING
When considering live subjects, factors such as the number of subjects, privacy
of patients, invasiveness of the procedure and most importantly obtaining the eth-
ical approval for research purposes has to be taken under consideration. Majority
of the captured images in clinics and hospitals do not have the ethical approval
and cannot be used in the study.
To avoid this problem, open source online databases have been suggested to be
used. Since the databases were collected for research purposes, the ethical process
has already been conducted. Another advantage of these databases is that it can
be used to test the compatibility of the image processing procedures on different
images with different settings.
Many databases have been investigated, including:
• Methods to Evaluate Segmentation and Indexing techniques in the field of
Retinal Ophthalmology (MESSIDOR) [134]
• Retinal Vessel Image set for Estimation of Widths (REVIEW) [135]
• Retinopathy Online Challenge (ROC) [136]
• Collection of Multispectral Images of the Fundus (CMIF) [137]
• UPOL Iris Image Database (UPOL) [138]
• Digital Retinal Images for Vessel Extraction (DRIVE) [139]
• Structured Analysis of the Retina (STARE) [140, 141]
The specifications for the above databases are discussed below.
The 1200 colour fundus images in the MESSIDOR database [134] were collected
across three ophthalmologic departments. The images were captured by 8 bits per
colour plane at 1440×960, 2240×1488 or 2304×1536 pixels using colour video
3CCD camera on Topcon TRC NW6 non-mydriatic retinograph with 45◦ FOV.
The Retinal Vessel Image set for estimation of width (REVIEW) [135] database
consists of four subsets, which are the High Resolution Image Set (HRIS), Vascular
Disease Image Set (VDIS), Central Light Reflex Image Set (CLRIS), and Kick
Point Image Set (KPIS). The 16 images are 1360×1024 to 3584×2438 pixels and
are manually marked for vessel segmentation by three observers.
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4. IMAGE ACQUISITION AND FUNDUS MAPPING
The Retinopathy Online Challenge (ROC) [136] database comprises of 50 im-
ages from patients with diabetes and signs of microaneurysms and/or hemorrhages.
These images were acquired using the default resolution and settings of the Topcon
NW 100, NW 200 or Canon CR5-45NM cameras.
The Collection of multispectral images of the fundus (CMIF) [137] database
consists of 17 images from healthy patients which were captured using Zeiss RCM250
camera with 40◦ FOV.
Due to limitations in time, for the purpose of this study, out of the many widely
used publicly available databases, three of the most popular databases have been
considered to investigate the suggested methodologies.
The first one is the Digital Retinal Images for Vessel Extraction (DRIVE)
database [139]. These images have been collected by Staal et.al. The forty collected
images have been captured by Canon CR5 non-mydriatic 3CCD camera with 45◦
FOV. Each image is 8 bits per colour plane at 768×584 pixels. The diameter of
the circular FOV is approximately 540 pixels.
The second database is the Structured Analysis of the Retina (STARE) database [140,
141]. In 1975, Michael Goldbaum initiated the collection of this dataset. Roughly
it contains about four hundred fundus retinal images. The images were captured
by TopCon TRV-50 fundus camera with 35◦ FOV. Each image is 8 bits per colour
plane at 605×700 pixels.
The third database is the UPOL Iris database [138]. It contains 384 Iris images,
including both right and left eyes. The RGB images are 24 bits, 576×768 pixels.
They were captured by SONY DXC-950P 3CCD camera and scanned by TOPCON
TRC50IA optical device.
It should be noted that since the obtained results have already been captured
and stored, this study will only concentrate on the software analysis.
4.3 Fundus Mapping
As mentioned previously, different Fundus cameras have different specifications
and therefore produce different images with a wide range of visual fields. There
are three different types of cameras, including the normal angle, narrow angle, and
wide angle of view cameras.
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4. IMAGE ACQUISITION AND FUNDUS MAPPING
The commonly used cameras which are considered to have normal angle of view
are 30◦. The features captured by these cameras are about 2.5 times larger than
their real life size [142].
The fundus cameras which magnify the images more than the normal angle of
view cameras are called narrow angle fundus cameras and have an angle of view
of 20◦ or less [142].
The new wide angle fundus cameras have the capability to capture wider retinal
images, rather than the traditional 30◦ angle fundus cameras. The FOV of these
cameras range from 45◦ to up to 140◦ FOV [142]. Nowadays, the use of such devices
has become quite popular as the wider FOV allows the ophthalmologist to detect
the diseases and the affected areas more accurately and so perform the treatments
earlier on, prior to disease progression. A disadvantage of this technology is that
the magnifying power of these cameras is less than the normal angle view cameras.
Moreover, this new technology is not accessible or price effective in remote ar-
eas or in many of the developing countries. Therefore a new approach has to be
developed to provide such information to the optometrists and the ophthalmolo-
gists.
Figure 4.4 illustrates the difference between the angles for each of the normal
angle (green), narrow angle (red) and wide angle (yellow) fundus cameras.
Figure 4.4: The difference between the view angle of normal angle, narrow angleand wide angle fundus cameras.
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4. IMAGE ACQUISITION AND FUNDUS MAPPING
Fundus mapping is a more time consuming approach in comparison to the use
of wide angle of view cameras. However, the main advantage of this technique is
that the wide angle view is obtained using more magnified images, revealing more
details to the ophthalmologists. This in turn may improve the accuracy of disease
diagnosis and treatment despite.
Moreover, the availability and cost effectiveness of the normal angle of view
cameras make the fundus mapping more feasible and desirable especially in devel-
oping regions.
Since in both developing an developed countries the typically used cameras are
the normal angle of view, it is advised to use images from normal angle of view
cameras for further investigation. As it can be seen in Figure 4.5, using multiple
images and combining the results expands the field of view and be more useful
than just a single image when it comes to disease diagnosis.
Figure 4.5: Importance of fundus mapping
In this section, merging multiple images in order to obtain a wider view of the
retina from the typically used 30◦ fundus camera has been considered.
This would be quite different to those of the previously performed montage
models represented in the literature. In general, montage model is thought to be
a time consuming procedure and difficult to perfect. There seems to be problems
with presence of artefacts to the montage images.
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4. IMAGE ACQUISITION AND FUNDUS MAPPING
To address these problems the following procedure has been suggested.
4.3.1 New Proposed Technique for Fundus Mapping
The objective of this section is to merge multiple fundus images together in order
to attain a wider FOV of the back of the eye. To do so, the geometric properties
or the eye is studied and the following method is introduced.
It is known that the back of the eye is curved; this property may be used to
introduce the following equations representing its horizontal curvature character-
istics. Moreover, in Figure 4.6 a geometric approximation of the retinal image is
illustrated.
Figure 4.6: Geometric representation of the proposed method for merging multipleretinal images. Radius of the Curve (R), Central Angle of the Curve (∆), CordLength (C), Tangent Length (T ), Middle Coordinate (M), External Distance (E)and the Middle (PM), Left (PL) and Right (PR) points can be viewed in theimage.
The Tangent Length, T , may be represented by the Equation 4.1. In this
equation, the Radius of the Curve is represented by R and the Central Angle of
the Curve in degrees (◦) is represented by ∆.
T = R× tan(
∆
2
)(4.1)
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4. IMAGE ACQUISITION AND FUNDUS MAPPING
The External Distance, E, can be shown as:
E = R
(1
cos(
∆2
) − 1
)(4.2)
Moreover, the Cord Length, C, is:
C = 2Rsin
(∆
2
)(4.3)
The Middle Coordinate, M , can be defined as:
M = R
(1− cos
(∆
2
))(4.4)
Lastly, L, which is the Curve Length Distance between PM to the V ertex
(Right angle triangle to T and R) can be written as:
L =R∆π
180(4.5)
Since in capturing the fundus image, the device and its specifications are known,
the Central Angle of the Curve in degrees (∆) would also be known. For example,
for the Fundus camera with 30◦ FOV, the Central Angle of the Curve for each
image would be 60◦ based on the inscribed angle theorem.
As a result of this, the Cord Length, C, would also be constant for all the
captured images. This agrees with the observation that all the retinal images
obtained from the same device with same setting appear to have the same shape
and diameter. Therefore, calculating the diameter of the fundus images, would
define the Cord Length value.
Based on these, the Radius, R, may now be calculated by re-arranging the
Equation 4.3 and resulting in Equation 4.6.
C = 2Rsin
(∆
2
)R =
C
2sin(
∆2
) (4.6)
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4. IMAGE ACQUISITION AND FUNDUS MAPPING
The equation found for the Radius, R, may now be substituted back into the
previously defined equation. Substituting Equation 4.6 into Equation 4.1 would
result in:
T = Rtan
(∆
2
)=C
2
(sec
(∆
2
))(4.7)
E, the External Distance, may now be defined by substituting Equation 4.6
back into the Equation 4.2:
E = R
(1
cos(
∆2
) − 1
)
=
((C
2sin(
∆2
)cos(
∆2
))−( C
2sin(
∆2
))) (4.8)
To simplify this further, the double angle formula may be used:
2sin(θ)cos(θ) = sin(2θ) −→ C
2sin(
∆2
)cos(
∆2
) =C
sin(∆)
Continuing on Equation 4.8, the External Distance, E, may now be:
E =
((C
sin(∆)
)−
(C
2sin(
∆2
)))
= C
(csc (∆)− 0.5csc
(∆
2
))(4.9)
Obtained result in Equation 4.6 can also be substituted into the Equation 4.4,
resulting in:
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4. IMAGE ACQUISITION AND FUNDUS MAPPING
M = R
(1− cos
(∆
2
))=C
2
((csc
(∆
2
))−(cot
(∆
2
)))(4.10)
Similarly, substituting the obtained result in Equation 4.6 into the Equa-
tion 4.5, would now result in:
L =R∆π
180
=C∆π
360sin(
∆2
) (4.11)
From this it can be said that, all the required properties for approximating the
retinal horizontal curvature can now be calculated.
It is now time to merge multiple of these images, increasing the FOV. To merge
multiple images, it is best to have some overlapping regions. The overlapping
regions ensure that the possible artefacts which may have been formed due to
inaccurate positioning in the result are reduced.
Figure 4.7: Approximation of retinal curvature using the Middle Coordinate
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4. IMAGE ACQUISITION AND FUNDUS MAPPING
The number of markers can also be increased as the OD and macula can be
detected. Using these markers in conjunction with the normal vasculature mark-
ers, the fundus map can be created with higher accuracy. Moreover, using the
approximation of the retinal curvature found via considering the Radius and the
Middle Coordinate of the eye, a 3D effect can be given to the outcome. Figure 4.7
illustrates this concept.
4.3.2 Implementation and Discussion
In order to map the fundus, it is important to define markers on each image so
that they could be used to overlap the image.
The advantage of the proposed approach over the readily available method-
ologies is that in the suggested case, the number of the markers has increased by
inclusion of the location of other retinal features. In the previous techniques, the
markers have been usually set based on the localized vessels. However in this case,
in conjunction with the localized vessels, other key features of the retinal image
including the location of OD and macula have also been used as markers.
Increase in the number of markers ensures that the error in creating the fundus
map is decreased and the overall image does not contain any duplicate images,
reducing the unwanted artefacts.
Moreover, the overlapping region has to be present, so that the combination of
the images could be achieved. Without the overlapping region, the images cannot
be placed next to one another as their location and the direction may be unknown.
The greater the overlapping region improved the accuracy of the fundus map,
but it also increases the computational complexity, reduces the speed of mapping
and also increases the need for using more images to create the full view of the
fundus map.
Based on this, it can be said that if time permits, fundus mapping could be
applied and used in developing or regional areas where the available resources
are limited. The advantage of this approach is that with minimal information;
knowing the fundus camera angle and the cord length of the taken images; a
simple yet reliable process can be applied. Moreover, since the number of markers
has increased, naming the location of the retinal vessels, OD and Macula, the
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4. IMAGE ACQUISITION AND FUNDUS MAPPING
accuracy of the fundus mapping has also increased.
4.4 Refraction Studies
In image acquisition and interpretation, the light characteristics and effects are
of great importance. In ophthalmological instrumentations, many different light
sources, settings and angles have been considered for capturing images. Based on
the results and the desired outcome, the best light characteristics has then been
chosen and applied to obtain images.
However, once the images have been captured, the effects of the light beams
on the accuracy of the results in the interpretation stage have not been considered
in many studies. It is essential to know more about the light characteristics when
analyzing the results as it directly affects the accuracy of the calculations as it is
one of the main variables. In this section, the light refraction and how it effects
the overall interpretation of results is reviewed and studied.
The light beams tend to refract when leaving a matter and entering another
matter with a different density values, which are commonly known as refractive
index values. Therefore, when studying the light, considering the light refraction
based on these refractive index values are crucial and many studies have missed
this in their interpretations.
Air
Eye
θ2
θ1
Equation 4.12 indicates the relation between the index value of the angle of
incidence and refraction when light passes through two different materials.
n1sin(θ1) = n2sin(θ2) (4.12)
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4. IMAGE ACQUISITION AND FUNDUS MAPPING
Where n1 and n2 refer to the refractive index values of the materials where the
light leaves and enters respectively. θ1 is the angle of incidence of light and θ2 is
the angle which the refractive ray created with the normal.
When light enters a material with higher refractive index, the angle of refraction
will be smaller than those of the angle of incidence, and hence the light will be
refracted towards the normal of the surface. However, if the refractive index of the
material is smaller, the refractive angle will be larger and light will be refracted
away from the normal.
n1 > n2 −→ θ1 < θ2
n1 < n2 −→ θ1 > θ2
4.4.1 Light Refraction In Retina
Based on the studies conducted by Hecht et.al. [143], the internal components of
the eye each have their own refractive index, hence the angle in which the light
enters the eye will not be the same as those reaching the back of the eye. As a result,
studying and implementing refractive index should be taken into consideration
while capturing or studying images.
Figure 4.8: Average light refraction indices for different regions of an eye.
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4. IMAGE ACQUISITION AND FUNDUS MAPPING
Using the Equation 4.12, the angle of refraction of the light as it passes through
each sections of the eye can then be calculated. The results can be viewed in
Table 4.1.
Table 4.1: Refractive Index of the light passing through different regions of theeye.
N1 1
N2 1.376
N3 1.336
N4 1.406
N5 1.337
N1N2
0.726744186N2N3
1.02994012N3N4
0.950213371N4N5
1.051608078
All 0.747943156
Using the results from Table 4.1 in the Appendix, the comparison between the
calculated angle of the refraction and the expected incident ray over 180◦ and 90◦
has been plotted and can be viewed in Figures 4.9 and 4.10 respectively.
Figure 4.9: Comparison of incident ray and refractive ray - 180 degrees
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4. IMAGE ACQUISITION AND FUNDUS MAPPING
Figure 4.10: Comparison of incident ray and refractive ray - 90 degrees
Figure 4.11 is an example of how the incident ray enters the eye and the refrac-
tive ray reaches the back of the retina. As a result of the difference in refractive
indices for each region of the eye, the bending of the ray is visible. The differ-
ence between the actual location of the ray and the expected location can also be
viewed.
Figure 4.11: Example of bending of the refractive ray in the eye
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4. IMAGE ACQUISITION AND FUNDUS MAPPING
The graphs clearly show that there is a significant difference between the inci-
dent ray angle and the refractive ray angle. This may suggest that the expected
location of ray will differ from the actual location of the ray reaching the back of
the eye.
In order to statistically determine the significance of the results obtained for
the refractive ray in comparison to those from the incident ray, in this section the
Analysis of Variance (ANOVA) has been performed and shown in Table 4.2.
Table 4.2: ANOVA of the incident and refractive rays for 0-90◦ range
Summary
Groups Count Sum Average Variance
Incident Ray 19 855 45 791.6666667
Refractive Ray 19 554.3631598 29.17700841 261.1563218
ANOVA
Source of Variation SS df MS F-value P-value F-crit
Between Groups 2378.497 1 2378.487 4.518 0.040 4.113
Within Groups 18950.814 36 526.411
Total 21329.301 37
In the Table 4.2, for each group of results, incident ray and refractive ray
over the range of 0-90 degrees, the number of variables (count), their overall sum,
average and variance have been calculated and displayed in the summary section.
Moreover, for comparing the results using ANOVA, the Sum of Squares (SS),
Degrees of Freedom (df), Mean Squares (MS), the calculated F-value, P-value and
critical F-value (F-crit) have also been calculated and presented in the ANOVA
section.
The p-values are commonly used to determine whether the null hypothesis
could be accepted and rejected. The null hypothesis in this case is that there is
no significant difference between the incident ray and the refractive ray and the
study is to prove whether that is true or not. Depending on the p-value this could
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4. IMAGE ACQUISITION AND FUNDUS MAPPING
be achieved.
P-values could mean:
• p ≤ 0.01 :very strong presumption against null hypothesis
• 0.01 < p ≤ 0.05 :strong presumption against null hypothesis
• 0.05 < p ≤ 0.1 :low presumption against null hypothesis
• p > 0.1 :no presumption against the null hypothesis
Based on the results illustrated in Table 4.2, the p-value is 0.040 which is less
than 0.05 but greater than 0.01. This means that there is a strong presumption
against the null hypothesis of no statistical significance between the two data sets.
This means that the two sets are significantly different and so when analytically
studying the images, the refraction of the light and its effect should also be con-
sidered.
Furthermore, the Fisher’s test (F-test) has also been found. The statistical
F-test determines whether the F-distribution is true under the null hypothesis.
The following is the formula for the one-way ANOVA F-test statistic:
F − test =Explained Variance
Unexplained Variance(4.13)
Using the Equation 4.13, the F-value has been obtained and as shown in Fig-
ure 4.2, it can be seen that the F-value is 4.518, which is slightly greater than
the critical F-value of 4.113. This suggests that the results may be significant at
the 5% significance level. Therefore, the null hypothesis can be rejected, suggest-
ing that there is strong evidence that the expected values in the incident ray and
refractive ray differ. This agrees with the results found for P-value.
Based on the above observations and results, it can be concluded that there
is significant different between the angle of incident and those of the refractive
angles and as a result should be taken under consideration when analysing the
outcomes. This may be beneficial to surgeons in their diagnosis of diseases as the
approximate location of the retinal features could be more accurately calculated
and determined using the angle of incident.
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4. IMAGE ACQUISITION AND FUNDUS MAPPING
4.5 Summary
In this chapter the preliminary step of image processing was considered. The
image acquisition, light refraction and improvements to the field of view using
fundus mapping were the main areas of interest in this section.
Different factors for device and light source selection were considered in order
to highlight their importance in image acquisition and its impact to the overall
outcomes of the project. After due consideration, the eye and fundus images
used in this case study were obtained from online sources, captured from wide
range of devices with different settings. The main sources were the DRIVE and
STARE databases. The images were chosen to test the flexibility of the suggested
methodologies and determine the accuracy of the obtained results.
Furthermore, the impact of fundus mapping and light refraction has been in-
vestigated. In the image capturing, the effect of light refraction is significant and
therefore has been carefully studied in this chapter. The results have shown that
there is a significant difference in the incident and refractive rays and therefore
the variation has to be considered in order to aid the medical practitioners by
detecting the actual location of the key features of the retina.
Diagnosis and treatments of the retinal diseases can also benefit from wider
view of the retina, using fundus mapping. The use of multiple images from nor-
mal 30◦ angle of view retinal fundus images have been considered to create the
retinal fundus map with the proposed approach. The accuracy mapping has been
increased by using multiple different markers, including the location of the vessels,
OD and macula.
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CHAPTER 5
IMAGE PRE-PROCESSING
Introduction Literature Review Thesis Outline Conclusion
Image Acquisition Image Pre-Processing
Implementation:
— Colour Separation
— Masking ROI
— Filtering/Noise Removal
— Image Sharpening
Further Modification:
— Contrast Enhancement
— Trimming
Feature Localisation Feature Extraction
Figure 5.1: Chapter Five Outline of Image Processing Stages
5.1 Overview
Improvements in the field of ophthalmology are indebted to advancements in im-
age capturing procedures and instrumentation. Previously, the visual inspection
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5. IMAGE PRE-PROCESSING
of the eye was the only source for disease detection and treatment. However, en-
hancements in imaging and its processing significantly changed these traditional
approaches.
Implementing image processing procedures would require image and data. Ma-
jority of the times, the raw data would not be sufficient for prognosis of the dis-
ease. As a result further processing would be required. Image processing can
provide more information via analysing the outcome and detecting information
which might be missed by visual inspection. In order to do so, the image has to
be prepared and modified in the pre-processing stage.
In the pre-processing stage the acquired images are manipulated and noises
removed in order to enhance the speed of detection and results obtained in the
consecutive stages of feature detection and extraction.
Despite the rapid technological progression and knowledgebase understanding
of the eye structures and the underlying processes; in many regions especially
the developing countries, current resources may still not be available. On many
other occasions, the capturing devices may not produce high quality images or the
obtained images, maybe too noisy. Hence, it is important to filter images while
preserving critical information.
Consequently, the accuracy of the images and their readability may be affected,
further resulting in poor study of the patients’ health and imprecise disease de-
tection. It is of great importance to ensure that the readily available resources
and obtained results are well prepared for further prognosis by experts in the best
possible timely manner.
As a result, it can be said that the preliminary stage of image pre-processing
and modification plays an important role in disease detection. In this chapter, the
pre-processing stage has been considered.
The procedures include colour separation of the captured images, masking
the ROI, filtering and noise removal of the images and sharpening them. The
performed procedures ensure that the ROI is accurately detected and the overall
precision of detection is enhanced.
Furthermore, new modifications including the contrast enhancement and trim-
ming regions for ROI is also suggested. The trimming regions are defined so that
the errors associated with the localisation of the ROI, such as the OD is reduced.
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5. IMAGE PRE-PROCESSING
Moreover, for betterment of the feature localisation, contrast enhancements are
suggested to be used.
5.2 Image Manipulation
Fundus retinal images captured are usually coloured images. Based on the work
conducted by Gonzales et.al. the coloured images are best to be converted into
either indexed or RGB (Red, Green and Blue) images [144, 145].
Gray scaled images have proven to reduce the complications and processing
time significantly. Therefore, the first step in image manipulation would be gray
scaling the RGB image. An example of such transition can be viewed in Figure 5.2,
part (a).
(a) Gray Scale (b) Red (c) Green (d) Blue
Figure 5.2: Colour band separation of a coloured image with respected histograms
The coloured image can also be separated in to its primary components of
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5. IMAGE PRE-PROCESSING
the red, green and blue. The obtained results and their respected histograms are
displayed in Figure 5.2.
Figure 5.3: Colour component separation of RGB image in horizontal direction
Observing the results in Figure 5.2 and those in Figure 5.3, suggests that the
red channel of the RGB image is saturated, while the blue channel is empty.
Therefore, for the purpose of this study, the green channel is chosen for further
investigation.
This result agrees with the previous findings in the literature [115, 117, 146].
Similarly, Al-Rawi et.al. [147] conducted a study to determine the performance of
each of the colour bands in the DRIVE database by plotting the Receiver Oper-
ation Curve (roc) on an improved matched filter. The results indicated that the
average roc area for the red, green and blue bands were 0.9348, 0.9352 and 0.9339
respectively, once again suggesting that the green band is the most appropriate
channel for digital retinal imaging.
Based on this finding, all the coloured images in this study have been grey
scaled and their green channel have been chosen and used for further processing.
Some sample results are included as part of Appendix B.
5.3 Masking
After deciding on the channel of interest, which is the green channel of the image,
the region of interest (ROI) needs to be defined using a mask.
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5. IMAGE PRE-PROCESSING
In retinal images, the main area of interest is surrounded by a black region.
Figure 5.4 illustrates a possible mask which could be used to define the ROI for
the sampled image. The white region in the mask is the ROI, while the black
region is the regions which are not of interest in the study.
(a) Image (b) Mask
Figure 5.4: Example of a possible mask for the sampled image
In the literature, there are many examples of how to mask the ROI. In many
studies, this mask is manually or automatically pre-defined and used for all images.
In other studies, methods such as the Otsu Method [148, 149] or Circular Hough
Transform [150] have been used to define the mask.
5.3.1 Otsu Method
Otsu method is based on the discriminate analysis and was first proposed by
Otsu in 1979 and since then was widely used in image processing applications.
Otsu method finds the optimal threshold in an image by thorough search of pixel
intensities for maximising the between class variances [148].
In the Otsu method, the image is separated into two classes of ”Object” and
”Background”, represented as C0 and C1 at the grey-level t.
C0 = {0, 1, 2, ..., t} C1 = {t+ 1, t+ 2, ..., L− 1}
Respectively, the within class variance 1, between class variance 2 and the total
variance are σ2W , σ2
B, σ2T . Based on the Otsu method, in order to find the optimum
1Within class variance is the weighted sum of the variances for each cluster.2Between class variance is the difference between the total and the within class variance.
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5. IMAGE PRE-PROCESSING
threshold, one of the following functions with respect to t should be minimised
[149].
λ =σ2B
σ2W
η =σ2B
σ2T
κ =σ2T
σ2W
Since η is the simpllest of the three equations it is usually chosen and so the
optimal threshold is defined as:
t = ArgMin(η)
η =σ2B
σ2T
κ =σ2T
σ2W
Otsu method has been implemented and the results can be viewed in part (a)
of Figure 5.1.
5.3.2 New Technique for Masking Using Thresholding
In the study, a similar approach to the Otsu method is suggested and implemented.
Since fundus images are obtained using different devices with different settings, a
universal adaptive approach is needed, where the ROI could be defined for each
individual image, regardless of the capturing device settings. Since each device
setting is unique, for a universal automated process, the images obtained have
to be individually analysed and therefore each image would need to be masked
separately in order for its ROI to be defined.
The suggested method is an adaptive thresholding technique. It is quite fast,
reliable and easy to perform. The first step is to obtain the intensity of the image
and plot the histogram of the plane of interest, which in this case is the green
plane.
Studying the histogram closely reveals that there is a large peak at the lower
pixel intensities, which suggests presence of a significant dark region in the image.
Since the surrounding region is coloured black and the ROI is lighter, defining that
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5. IMAGE PRE-PROCESSING
region and masking it would result in separation of the two regions.
To define the mask for the background region, a threshold is set where the
first major minimum occurs in the smoothed histogram. An example is shown in
Figure 5.5.
Figure 5.5: Histogram used to determine a threshold for masking the ROI
Once the threshold is set, all the pixel values in the image which have the pixel
intensities below the defined ROI is set to ”0”, and any values above it, is set to
”1”. The result is the creation of a binary mask, defining the ROI. The mask is
smoothed out by removing or filling up any noise which might appear as black
”holes” in the image.
An example of the proposed mask is shown in Table 5.1. In the figure, the re-
sults obtained using the proposed technique is compared with the results obtained
by implementing the Otsu method.
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5. IMAGE PRE-PROCESSING
Table 5.1: Comparison of the masks formed by Otsu method and the suggestednew Thresholding method.
Technique Mask Masked Image
Otsu Method
New Thresholding Method
The comparison suggests that the proposed method is as accurate as the pre-
viously suggested Otsu technique. This approach has been implemented on more
than twenty images and the results indicate an accurate localisation of ROI for all
cases. The obtained masks have been included in the Appendix C.
Furthermore, in cases such as this one, where the two clusters are easily distin-
guishable, simpler yet reliable approach of thresholding is desirable. The suggested
approach is also faster and computationally less complicated in comparison to the
Otsu method as it only considers the occurrence of first major minimum instead
of calculating the minimised variances of different sections of the image. There-
fore the proposed technique can be used to define a mask for ROI as a universal
automated approach.
5.4 Filtering
Despite the presence of hardware filters, the obtained images are not ideal and
are still noisy. Therefore, it is essential to filter images and minimise noise prior
to any further processing. Since the used images are from open source databases
and so no further hardware filter may be implemented. Moreover, software filters
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5. IMAGE PRE-PROCESSING
can be implemented on all images irrespective of the specifications of the image
capturing devices. Therefore, the study concentrated on software analysis of the
images and this section software filters have been applied and the details are as
follow.
5.4.1 2D Fast Fourier Transform
To enhance the processing time and reduction in computational complexity, the
2D Fast Fourier Transform (FFT) is suggested to be used as a filter. FFT is
computationally simpler because the filter is multiplied in frequency domain, while
in spatial domain it would have to be convoluted, therefore the FFT would result
in faster response time. It has been implemented and used as the basis of multiple
upcoming stages in this thesis.
FFT is an important tool in signal and image processing. In order to filter a
two dimensional image, it is best to convert the image to its frequency domain.
2D FFT is simply the FFT which has been applied to one direction followed by
the FFT implemented in another direction of the data. 2D FFT represents the
frequency spectrum in both dimensions, allowing filtering operations to be visually
studied.
To implement the 2D FFT, the following Equation 5.1 may be used:
F (u, v) =1
MN
M−1∑x=0
N−1∑y=0
f (x, y) e−j2π(uxM
+ vyN ) (5.1)
Similar to the 2D FFT, the inverse 2D FFT is simply Inverse FFT (IFFT) which
has been applied to both directions of the data. The Equation 5.2 represents the
2D IFFT:
f (x, y) =M−1∑u=0
N−1∑v=0
f (u, v) ej2π(uxM
+ vyN ) (5.2)
To better visualise the results obtained via implementing the 2D FFT, an
example is shown in Figure 5.6.
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5. IMAGE PRE-PROCESSING
(a) Image (b) Magnitude Plot (c) Phase Plot
Figure 5.6: Implementing 2D FFT on a retinal image
Studying the magnitude plot of the obtained 2D FFT, reveals that most of
the energy is concentrated in the centre of the image. This corresponds with low
frequency data in the frequency domain, suggesting gradual changes in the image.
Moreover, in the result, there are no sharp lines away from the centre of FFT,
suggesting that there is no great energy in the higher frequencies.
The phase of the FFT is somewhat hard to interpret visually and generally looks
like noise. However, it holds a great deal of the information needed to reconstruct
the image. Therefore, including the phase plot in the results is essential as the
output of the research should not alter the original data and should have the
capability to reconstruct it if necessary.
The results obtained in this section are the preliminary stage of the processes
in the next Section and Section 7.1. Therefore, this process has been implemented
on over twenty different images and the results are included in Appendix D.
5.5 Sharpening the Retinal Image
There are times where certain features of the image need to be enhanced in order to
be detected. An example could be when the vessels in the retina are to be detected.
In such cases, it is advised to sharpen the image prior to feature localization. To
sharpen an image, the filtered image may be added to the original image. This
would result in highlighting the key features and emphasising on their edges.
In the study, to sharpen the image, the use of the 2D FFT and convoluting it
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5. IMAGE PRE-PROCESSING
with a kernel of set value is suggested. It should be noted that for different image
sizes the kernel sizes may also vary based on the level of details required. For the
purpose of this study sSeveral different kernel sizes have been examined. In order
to observe the effect of the kernel, two kernels of 10×5 and 3×2 have been used
to compare their effects and the obtained results are displayed in Table 5.2. They
provided the best clarity in the results and observations of the studied images and
therefore were chosen to be implemented in the consecutive stages of the study as
well. The two kernels have been shown in Figure 5.7.
(a) 10×5 (b) 3×2
Figure 5.7: Used Kernels
Kernels are used to dilate the images. Dilation is a commutative process,
operating to grow or thicken and object in a binary image [144]. It can be used to
enhance a certain feature of the image. Opposite to dilation is erosion, in which
the object in the binary image shrinks or thins [144]. It may be used to remove
unwanted smaller objects, including the non-variable holes or dusts [144, 151]. It
should be noted that during both the erosion and dilation process, small cells,
noise and some details are lost, but the essential characteristics remains [151].
Comparing the outputs displayed in Table 5.2, it can be seen that the results
for the two suggested kernels slightly vary. The smaller kernel size, results in a
better sharpened image and so more details can be viewed in this case. On the
other hand, if larger details are of interest, the use of larger kernel size would be
more appropriate.
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5. IMAGE PRE-PROCESSING
Table 5.2: Sharpening the retinal image using 2D FFT
10 × 5 kernel 3 × 2 kernel
Green Channel of Image
Magnitude Plot
Real Part of Spectrum
Imaginary Part of Spectrum
Filtered Image
Subtract Filtered Image from
Original Image
Inverse - Subtract Original Im-
age from Filtered Image
Moreover, the two kernel sizes which were selected and implemented were just
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5. IMAGE PRE-PROCESSING
samples so that the effects of the variation in kernel size on the overall outcome
could be visualized. For different cases and capturing devices, this kernel size may
vary and so should be reset if necessary in order to provide the sharpest image
possible. This process has been implemented on all the images within the DRIVE
databas. The results are included in Appendix E and indicate similar outcomes
as the above discussion.
5.6 Trimming Regions
Many of the studies performed previously suggest that the outcomes from the
automated feature localisation stage are not 100% accurate. To overcome this
problem and enhance the results, in such cases the manual input from the user is
suggested to be used. The downfall of this would be that the outcome might vary
depending on the individuals with different experience levels. Moreover, in cases
where the expert opinion is not available the semi-automated system might not
provide the ideal result. In the study, for a fully automated detection process it
has been suggested to consider and resolve errors which result in the misdetection
of the feature of interest.
(a) (b)
Figure 5.8: Two examples of retinal fundus images. If observed closely, a brightfringe can be seen at the left hand corner of the image (b) which may result ininaccurate OD detection. The bright fringe cannot be seen in the image (a).
One of the common problems with obtained images is the unbalanced bright-
ness in the fringe of the rim, which is caused when the patients do not place their
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eyes tightly against the capturing device. This leads to misdetection of the ROI,
which in this case is the OD.
In this study, 10% of the images with the database illustrated signs of fringe
noise presence.
To overcome this problem, study conducted by Zhang et.al. in 2010 [152]
has proved to be vital for system’s uniformities and accurate detection of ROI.
The authors introduced a pre-processing step, known as the fringe removal. They
proposed a trimming circle, where its center and radius were defined based on the
least-square fitting technique, previously suggested by Kasa [153].
The suggested trimming circle is represented by Equation 5.3, has its center
located at (Cx,Cy ) and its radius is shown in Equation 5.6. It should be noted that
in order to remove all the bright regions caused by ambient light, the estimated
radius is set to be smaller than the calculated radius [152].
X2 + Y 2 + (AX) + (BY ) + C = 0 (5.3)
Cx =−A2
(5.4)
Cy =−B2
(5.5)
r =
√A2 +B2
4− C(5.6)
Furthermore in this study, the OD region was considered to be the 0.5% of
the bright spots in the trimmed fundus image. The centroid of the region was
considered as the center. The ROI boundary was limited by considering a radius
twice those of the normal OD [152].
Implementing the circle on the image and then processing the fundus image to
detect the OD, resulted in successl rate of 96% detection. In the remaining cases,
the manual input of the user, was used to adjust the region of interest [152].
Since only 5% of the pixels has been considered as OD, this method may or may
not have the desired accuracy as different image intensity may reduce the precision
of localisation. Moreover, the OD was approximately determined by considering
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a boundary twice hose of the normal OD. As a result, the chosen OD pixels may
not be correctly selected and so critical information may have been lost. Therefore
this study introduced a new methodology in localising the exact OD region, more
details have been provided in Chapter 8.
To further improve the accuracy of detection, in this study another circular
trimming region has also been suggested. Since not all fundus images appear to
be circular, the trimming region is further modified to provide the best possible
outcomes in such cases. More details are provided in section 5.6.2
5.6.1 Circular Trimming Region
As it can be seen the previously suggested technique was not ideal and there was
still a need for manual user input. To improve the results and the success rate of
detection, the previously proposed procedure has been re-examined and the new
approach suggested.
As it is known, a circle, centerd at (h, k) is represented by the equation:
(x− h)2 + (y − k)2 = r2 (5.7)
The result of expanding and rearranging this equation would be:
x2 + y2 − (2hx)− (2ky) + h2 + k2 = r2
x2 + y2 − (2hx)− (2ky) + h2 + k2 − r2 = 0 (5.8)
Comparing Equation 5.3 with that of Equation 5.8 suggests similarities between
the two, and hence equating them would provide:
X2 = x2 −→ X = x (5.9)
Y 2 = y2 −→ Y = y (5.10)
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Now compare the third and fourth terms:
AX = −2hx
Based on Equation 5.9, it can be said that: X = x, therefore:
A = −2h −→ h =−A2
(5.11)
Moreover:
BY = −2ky
Based on Equation 5.10, it is known that: Y = y, therefore:
B = −2k −→ k =−B2
(5.12)
Comparing Equation 5.3 with the Equation 5.8 indicates that the constant
term is:
C = h2 + k2 − r2 (5.13)
Substituting Equations 5.11 and 5.12 into 5.13 and simplifying would result in:
C =
(−A2
)2
+
(−B2
)2
− r2
=A2
4+B2
4− r2 (5.14)
Using Equation 5.14, the variable r is made the subject:
r2 =A2 +B2
4− C
r =
√A2 +B2
4− C (5.15)
In Table 5.3, the suggested trimming region by Zhang et.al. and the suggested
trimming circle in the study is represented.
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Comparing the two trimming regions, concludes that the location of the center
for both cases is the same and is represented by (h, k); however, as it can be seen
in Table 5.3 their radii are defined differently. Under the same conditions and
specifications, the calculated radius of the new proposed trimming technique is
smaller than those suggested by Zhang et.al.
It should be noted that similar to the previous technique, the radius of the
proposed trimming circle is also set to be smaller than the estimated radius. This
marginal variation in radius ensures that all the bright regions have been removed
from the image. The amount for the variation would depend on the number of
image pixels, general location of the OD in the image and its distance to the black
boundary.
Table 5.3: Comparison table of the proposed trimming circle with those suggestedpreviously in literature
Trimming region (Zhang et.al.) Proposed trimming circle
Equation X2 + Y 2 + AX +BY + C = 0 (x− h)2 + (y − k)2 = r2
CenterCx = −A
2
Cy = −B2
h = −A2−→ h = Cx
k = −B2−→ k = Cy
Radius r =√
A2+B2
4−C r =√
A2+B2
4− C
Therefore, when analyzing a new set of database, with different capturing set-
tings, it is suggested to visually observe a few of the retinal images so that if
required, the variation in radius could be changed for all the images within that
database.
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5.6.1.1 Implementation
In order to implement the trimming region on wide range of databases and retinal
images with variety of resolutions and capturing settings, it is essential to create an
algorithm in which the required variables are detected for each individual image.
The key requirement in plotting the region is to locate its center. The steps
undertaken to determine the center of the analysed retinal images are as follow:
1. The first non-zero pixel is determined. The first pixel would be the one which
is not black and so is not part of the background black boundary. This would
represent the most left non-zero pixel in the retinal image.
2. Last non-zero pixel is then found. This would be the last pixel which is not
black and is located on the right hand side of the image.
3. To estimate the center, the horizontal and vertical pixel locations of the pin-
pointed pixels are used and the middle values are calculated and considered
as a preliminary location of the center.
4. Using the horizontal middle value found previously, the first and last non-zero
values in vertical directions are determined. These points would represent
the furthest top and bottom points where the pixel values are still non-zero.
5. Similarly, using the vertical middle value found in step 3, the first and last
non-zero values in horizontal directions are also determined.
6. Once the points are determined, their average values are taken, resulting in
the re-calculated center location of the trimming region. The final center
point can be seen as orange (+) sign on the images where the trimming
regions are plotted.
The other necessary value needed to plot the region, would be its radius. The
radius can easily be calculated using the difference between the number of pixels
from the center to any of the four previously founded points in the top, bottom,
left or right hand side.
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Figure 5.9: Example of results obtained for plotting a trimming region. Thegreen (+) signs indicate the preliminary estimated points. The orange (+) signsindicate the calculated points, including the estimated center. The yellow circle isthe trimming region which has been plotted using the information.
Based on the above process and the obtained values for the radius and center
location, the trimming region may now be plotted as depicted in Figure 5.9. More-
over, since the above process is repeatable, it can be implemented on any given
RGB image, with any specifications.
5.6.1.2 Results and Discussion
The proposed circular trimming region has been implemented and results have
been displayed in this section. The first point to consider is to explain the im-
portance of using modified radius value instead of the estimated radius. Table 5.4
displays the results obtained from implementing the suggested trimming circle
using both estimated and modified radius values.
The left hand column represents the results obtained from implementing the
circular trimming method using the calculated radius, while the right hand column
shows the results for the same image, when a smaller radius has been used. This
small change in radius ensures that all the fringes are removed and the OD is
accurately detected.
The variation between the estimated radius and the implemented radius could
be defined based on the ROI. As mentioned previously, this type of error mainly
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occurs when detecting the OD. Therefore, it is important to choose the radius so
that all the bright fringes are removed while the OD remains untouched.
Table 5.4: OD localisation using trimming circle
Trimming Region
(Estimated radius)
Trimming Region
(Modified radius)
Trimming Region
(Yellow)
Circularly Trimmed
Brightest Regions
(Possible OD)
Estimated OD
(Red)
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Observing the results suggests that the use of proposed circular trimming re-
gion with modified radius would provide more accurate results for localization of
OD. However, not all fundus images are circular and so this method would be
insufficient and the results imprecise. Therefore, there is a need for another ap-
proach and so the author has suggested the use of elliptical trimming region when
the outcomes of the circular trimming is invalid.
5.6.2 Elliptical Trimming Region
As shown in Figure 5.10, depending on the setting of the capturing device, the
trimming region, may not be circular, and may be more of a truncated shaped.
Figure 5.10: Examples of retinal images using different capturing devices.
If the radius based on the short axis was calculated, the circular region may be
similar to those found in Figure 5.11. The obtained results clearly indicate that
some of the ROI has been cut out and hence the localisation will be inaccurate as
critical information has been removed.
(a) (b)
Figure 5.11: (a) Inaccurate circular trimming circle (yellow) for an elliptical shapedcaptured fundus image. (b) Trimmed image.
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To avoid the loss of critical information, it is essential to revise the suggested
methodology. Hence, the long axis has to be considered and used to calculate
the radius of the estimated trimming circle or a secondary analysis has to be
implemented using an elliptical trimming region. Using the long axis to calculate
the radius of the circle would provide results as indicated in Figure 5.12.
(a) (b)
Figure 5.12: (a) Accurate circular trimming circle (yellow) for an elliptical shapedcaptured fundus image using long axis as the radius. (b) Trimmed image.
The results obtained in Figure 5.12 suggests that a circular trimming circle
may be sufficient to remove all the noise close to the black boundary. However, it
is also possible to use an elliptical trimming region as shown in Table 5.5.
Table 5.5: Proposed Circular and Elliptical Trimming Regions
Proposed Trimming Circle Proposed Trimming Ellipse
Equation (x− h)2 + (y − k)2 = r2 (x−h)2
a+ (y−k)2
b= 1
Center (h, k) (h, k)
Radius r =√
A2+B2
4−Cx−Radius = a
y −Radius = b
Referring to Table 5.5, it is apparent that the two regions only vary in radius,
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where in an ellipse both short and long axis are taken into consideration, which is
basically what is assumed in the previous section for the circular trimming of an
oval shaped fundus image.
5.6.2.1 Implementation
The implementation would be similar to that of the circular trimming region,
with the minor variation of short and long axis. Table 5.6 shows examples of
the results obtained for both circular and elliptical trimming regions for different
fundus images.
Table 5.6: Implementation of both circular and elliptical trimming regions forcircular and elliptically shaped retinal fundus images
Circular Image Elliptical Image Elliptical Image
Trimming Region
(Yellow - Circular
Green - Elliptical)
Circularly
Trimmed
Elliptically
Trimmed
5.6.2.2 Results and Discussion
The obtained results suggests that for fundus images which appear to be circu-
lar, both the elliptical and circular trimming regions would approximately be the
same, hence the use of circular trimming region which has less variables would be
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sufficient and provide good accuracy. However, for the truncated retinal images,
the two trimming regions of circular and elliptical will not be the same. In such
cases, the use of elliptical trimming region is suggested.
If the ROI is OD and the objective is to localise it, elliptical trimming region can
provide an accurate but faster results than those of the circular trimming region.
As discussed previously, this is due to the fact that the radius of the circular
trimming region may need to be re-calculated. However, if the elliptical region is
implemented, all the fringe noises are removed with the preliminary calculation of
the both short and long axis radii without the need for any recalculations. The
results can be seen in Table 5.7.
Table 5.7: OD Detection for Circular and Elliptical Trimming Region
Trimmed Image OD Detection
Circular
Trimming
Region
Circular Image
Elliptical Image
Elliptical
Trimming
Region
Circular Image
Elliptical Image
This is only true for cases where the OD or Macula localisation and their anal-
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ysis are of importance. In other cases, such as vasculature detection, it is essential
to preserve data and information as much as possible. As a result, using ellipti-
cal trimming region, where great extent of data is removed, affects the accuracy
of detection significantly and is undesirable. In such cases, circular trimming re-
gion might be the better option. It should be noted that to improve the results
even further, multiple radius values may be used for individual images in order to
determine the best radius for the circular trimming circle.
In conclusion, using the suggested trimming circle and ellipse with adjusted
radius and applying them to the variety of data bases and re-examining the OD
detection using the proposed methodologies, suggested that the localisation is of
more accuracy and the detection rate is now 100% when fringe noise is present, in
comparison to the studies previously conducted in the literature.
There are also times when the whole image is too dark or too light. In such
cases, the detection of features become more difficult as the boundaries would be
less defined. To improve the detection precision in these circumstances, the image
intensity has to be adjusted by enhancing the image contrast. More details are
provided in the next section.
5.7 Contrast Enhancement
The previous section considered the effect of a localized variation in contrast and
how to overcome this problem using trimming regions. The overall results were
promising and the precision of the localization of ROI was improved.
However, the variation in contrast is not always confined to a specific region
of the image. The whole image may appear to be brighter or darker than the set
specification of the system and as a result the accuracy of detection is reduced for
the localization of ROI. In such cases another approach has to be taken.
Moreover, the flexibility of the automatic detection process is critical in iden-
tifying the ROI. There are occasions where the ROI detection has been affected
by the variation in contrast of the obtained images. In such cases, the proposed
system should still be able to proceed and perform its function successfully.
In majority of the studies performed previously, the grey scale image or the
green band of the coloured image was chosen for further processing. Similarly,
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initially for this study, the green band was chosen to detect the features of interest.
However, there were times where the ROI detection proved to be inaccurate or
unresponsive. Visually revisiting and studying these images indicated that the
variation in contrast, might have been the cause.
Illumination in fundus retinal images is uneven and so the images may appear
to be brighter or darker. The variation in the lighting is dependent on the image
capturing system or the response of the retina itself. The non-uniform illumina-
tion adversely affects the ROI localisation precision and may even result in mis-
detection. In such cases it is essential to consider various contrast enhancement
techniques for re-adjusting the image colours. Once this stage is complete, the
remaining detection processes may be re-implemented and results obtained. Con-
trast enhancement is crucial in medical field as it can reveal information which
might have been otherwise missed or hidden from view.
There are two widely used approaches in contrast enhancement, the linear con-
trast stretching 1 or the histogram equalization 2. In the linear contrast stretching,
the dynamic range of the image is adjusted, while in the histogram equalization,
form the integral of the image histogram, the input and output relation is ob-
tained [154]. In this study, the most common approach in field of medicine is
chosen for further investigation which is the histogram equalization method.
From the available histogram equalization techniques, the Adaptive Histogram
Equalization (AHE) and the Adaptive Contrast Enhancement (ACE) are the most
popular [154].
The AHE algorithm uses the local histograms obtained from the gray values
of pixels. The image is separated into blocks. A particular pixel is enhanced by
interpolating its mapping function with its neighbouring four blocks [154].
The ACE method uses the unsharp masking technique in which the image is
separated into two masks using the low frequency filter. The high frequency mask
is obtained by subtracting the low frequency mask from the image. The amplified
1Linear contrast enhancement or linear contrast stretching is when the original values areexpanded into a wider range. As a result the subtle changes in variation become more apparent.
2Histogram Equalization is when both shape and distribution is taken under consideration.Each level in the displayed image has to have approximately equal number of pixels. This isachieved by stretching the regions with more pixels more than those with few pixels in thehistogram.
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high frequency mask is then added to the original image in order to enhance the
image contrast [154].
Since in the histogram has been already obtained, the AHE technique which
uses the information obtained from a local histogram to map the gray values of
the pixels has been chosen and implemented.
Depending on the feature of interest, the approach undertaken for contrast
enhancement may vary. Therefore in the study a few different histogram equaliza-
tion methods have been implemented as the interest regions varied significantly in
characteristics.
5.7.1 New Necessary Step
To overcome the uneven illumination, the contrast of each of the images is to be
modified. For each of the contrast enhancement methods, the histogram of the
original image is changed and adjusted to form a new histogram known as the
Desired Histogram (DH).
5.7.1.1 Intensity Adjusted
The first approach has been to modify the intensity variance of the image, such
that 1% of the low and high intensities of the gray scaled image is saturated.
(a) Original Histogram (b) Intensity Adjusted
Figure 5.13: Example of the effect of Intensity Adjustment
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Figure 5.13 illustrated an example of the changes which occur to the histogram
when its intensity is adjusted. This may be useful, in particular when the original
image is quite dark and so the affect will increase the contrast of the overall image.
However, when noise is present in either of the low or high intensity bands, this
method may not be reliable as it magnifies the error.
5.7.1.2 Histogram Equalization
The second approach is the Histogram Equalization (HE) method [144, 155]. It
basically involves modifying and equalizing the intensity of each image so that the
illumination effects have been minimized. An example of the effect of HE method
on a histogram can be viewed in Figure 5.14.
(a) Original Histogram (b) HE
Figure 5.14: Example of the effect of Histogram Equalization
In this section, a flat DH is formed and applied to the image [144]. It is as
follow:
Desired Histogram =ones(1, n) ∗ Pdt(Size(A))
n(5.16)
Where Pdt is the product of array element in A.
The DH ensures that the grey scale transformation T is minimised by:
|c1(T (k))− c0(k)| (5.17)
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In the Equation 5.17 the c0 is the cumulative histogram of A and the c1 is the
cumulative sum of DH for all intensities of k.
It should be noted that this equation is constrained such that T must be
monotonic. Moreover, c1 (T (a)) should not overshoot c0 (a) by more than half
the distance between the histogram counts at a.
To map the grey level into their new values, the DH uses the b = T (a) trans-
formation.
5.7.1.3 Adaptive Histogram Equalization
The final approach which is in most cases a more effective method than the HE,
is the Adaptive Histogram Equalization (AHE) method. It is more commonly
known as the Contrast Limited Adaptive Histogram Equalization (CLAHE) as it
concentrates on a small region of the image. It follows the work performed by
Zuiderveld [156].
Figure 5.15 shows the effect of CLAHE when it is applied on a sample his-
togram.
(a) Original Histogram (b) CLAHE
Figure 5.15: Example of the effect of Contrast Limited Adaptive Histogram Equal-ization
CLAHE separates the image into smaller regions and works in enhancing the
contrast in each of those sections, therefore the histogram output approximately
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is the same as the specified DH. In order to remove the boundaries formed by each
region, the bilinear interpolation can then be applied to smooth out the output
image.
This technique is quite useful as it minimizes the amplification of the noise
present. This is due to the fact that the image is separated into smaller regions
and each section is analyzed separately, reducing the effect of the noise to the
surrounding regions. However, due to computational difficulties, this process may
also take longer.
5.7.2 Results and Discussion
In this section, the effect of each of the different contrast enhancement methods
discussed above is visualised. A sample image has been selected to represent the
effect of each method and how enhancing the contrast of the image may help in
accurate localisation of the ROI.
The outcome is clearly depicted in Table 5.8. As it can be seen, previously
while detecting the OD as the ROI using the green band of the image; the result
was inaccurate. However, after implementation of the three approaches, the OD
was correctly localised.
The variation in contrast and the effect of each of the techniques is apparent
in the image. As mentioned earlier, depending on the area of the interest, the im-
plemented methodology can then be chosen. For example for the OD localisation,
it is better if the image is not too bright since the OD is the brightest region in
the retinal image. If the image is too bright, there is a possibility of misdetec-
tion. However vasculatures are best visible in high contrast and bright images.
Therefore depending on the feature of interest, the chosen methodology to adjust
contrast could vary.
For the purpose of this study, the discussed methodologies proved to be ad-
equate and the implemented approaches tend to provide sufficient information.
For different applications, one or two of the methodologies have been useful. For
example, in vasculature detection the HE method proved to reveal more intricate
details, while the AHE method was used primarily for OD localisation.
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Table 5.8: OD localization for contrast enhanced images.
Image OD localisation
Green Channel of
Image
Intensity adjusted
image
Histogram
equalised image
Adaptive histogram
equalised image
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5.8 Summary
In this chapter the pre-processing stage of image processing was applied, so that the
overall accuracy of feature detection could be enhanced for automated processes
with minimal user input.
The common processes including the conversion of the images from colour to
grey scaled and green band images, masking, filtering and image sharpening were
implemented.
Based on the previous literature, the coloured images are best to be converted
into the grey scaled images or to their primary components. This enhances the
processing time, while preserving the image details. The study agreed with the
literature that green channel of the image provided the best level of details for the
purpose of this research and was selected to be used for the consecutive steps of
the image processing process.
To define the ROI, a new thresholding procedure was suggested to mask the
images. This new procedure automatically locates the location of the first major
minimum in the image, separating the ROI from the black background. The
obtained results are promising, demonstrating rapid but exact localisation of the
ROI for masking the retinal images. The accuracy is very similar to the Otsu
method; however, it is computationally less complicated and faster.d
The use of 2D FFT filter was suggested also suggested in this study as a
software filter in order to improve the processing time in the consecutive stages of
the image processing. Using the 2D FFT in conjunction with a kernel was then
used to sharpen the image so that the key features of interest in the fundus image
could be enhanced. The smaller kernel size proved to provide more details about
the image while the larger kernel size displayed the overall outlay of the features.
This characteristic has been used in the coming chapters for localisation of vessels
in the retinal images.
Furthermore, two main factors which may lead to imprecise localization or
misdetection of ROI were considered, including the presence of fringe noise or
localization of a desired feature in low contrast retinal images.
The fringe noise mainly occurs during the capturing where the ambient light
affects the image when the patients’ eye is not placed directly in front of the device.
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The presence of this bright spot may result in misdetection of the ROI, in particular
when localizing the OD. To eliminate this error, the use of new trimming regions
was suggested so that depending on the shape of the fundus image, the illuminated
noise could be removed automatically. The results for both circular and elliptical
trimming regions proved to be promising, with improvements in overall rate of OD
detection to 100% in comparison to the previously conducted studies.
The other factor which was considered in this chapter was to implement con-
trast enhancement methodologies, so that the ROI could be more easily distin-
guished and localised. Majority of the researches tend to not perform this step
and only use the grey scaled or green band of the colours image in the analysis.
However, for an automated system, it was observed that enhancing the image con-
trast can play a significant role in localisation of the feature. Different histogram
equalization approaches were considered and implemented, including the Intensity
Adjustment, Histogram Equalization and Adaptive Histogram Equalization. The
precision for feature detection has improved as a result, especially when Histogram
Equalization was used in vascular detection and Adaptive Histogram Equalization
was used for OD localisation.
In conclusion of the chapter it can be said that the pre-processing stage im-
proved the outcome of the detection process and increased its success rate. It also
reduced the amount of manual user input needed for feature localization system
of retinal images.
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CHAPTER 6
IRIS AND PUPIL LOCALISATION AND EXTRACTION
Introduction Literature Review Thesis Outline Conclusion
Image Acquisition Image Pre-Processing
Feature Localisation:
—Iris
— Pupil
Feature Extraction:
— Center
— Area
Figure 6.1: Chapter Six Outline of Image Processing Stages
6.1 Overview
The next main step in image processing after image acquisition and pre-processing
is feature localisation and extraction. In the literature review chapter, some of the
key features of the eye have been identified to be important in many applications
of ophthalmology and biometrics. In this chapter, Iris and Pupil of the eye have
been considered as the features of interest.
Recent studies and applications of biometrics authentication which relates to
the human characterisation and identification, suggests that uniqueness of individ-
ual Iris pattern can be used to separate and distinguish people with extremely high
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6. IRIS AND PUPIL LOCALISATION AND EXTRACTION
accuracy. Hence, many methodologies have been introduced and implemented in
order to localise the Iris.
Accurate detection of the Pupil boundary can also play a significant role in
the field of ophthalmology as well as biometrics. Accurate detection of the Pupil
and Iris boundary specify the exact Iris region which can then be used for exact
pattern extraction as well as disease diagnosis, treatment and monitoring stages.
For example, during a treatment procedure, such as the case of cataract surgery,
detection of the marginal variations of the size of Pupil boundary may minimise the
occurrences of complications to a great extent. Hence it is important to accurately
detect the Pupil boundary and its changes.
Based on the above factors, new methodologies for fast localisation of the Iris
and Pupil have been proposed in this chapter. Moreover, approaches have been
suggested to automatically detect and measure the center and area of the features
so that medical practitioners could use this information to identify changes due to
disease or complication.
The two essential steps in image processing, the feature localisation and feature
extraction for Pupil and Iris are considered in detail and different approaches are
suggested and applied for the betterment of the final outcomes.
6.2 New Technique for Iris/Pupil Localisation
Exact boundary detection of the Iris and Pupil restricts the affected area and fur-
ther analysis may provide the ophthalmologist with more insight to the severity of
the disease. The information may aid ophthalmologists with all level of experience
to better diagnose and treat the patients.
As discussed in section 3.2.2.1, many different approaches have been considering
Iris and Pupil detection, each having their own advantages and disadvantages.
Amalgamates process has been suggested to be used in this section. This is to
ensure that the actual desired region is selected and more accurately detected. In
order to do so, the results of two different techniques have been fused together,
creating a single mask which segments the ROI, which is then applied to the
original data to define the ROI. In this case the Iris and Pupil have been detected
using this methodology.
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Figure 6.2 illustrates the flowchart of the overall procedure of the proposed
technique by the author for detection of Iris and Pupil [9, 12].
EyeImage
Acquisition
Image
Pre-Processing
Iris/Pupil
Localisation
Method 1 Method 2
Mask1 Mask2
Overall Mask
(Mask1 + Mask2)
Matching
(with previous data)
Interpretation
(Feature Extraction)Display
Complication?
Alarming System
Revise
Procedure
Continue
NoYes
Figure 6.2: Proposed steps for Iris and Pupil localisation.
Normally any single approach may have its own advantages and disadvantages,
affecting the overall outcome. To check the validity of the result and verifying that
the detected region is in fact the desired ROI, it is best to double check the outcome
with another methodology as well.
From the studied literature, two methodologies have been chosen and imple-
mented to investigate and prove this concept. The thresholding approach sug-
gested by Masek [107] and the active contouring procedure introduced by Rit-
ter [108] have been implemented following the suggested. Detailed explanation of
the two processes and their advantages were included in Section 3.2.2.1.
Figure 6.3 is an example of the possible results which might be obtained from
two different techniques. The results for Iris localisation from technique one is in
green and the technique two is in red. It can be seen that each of the techniques are
not ideal and have missed some critical information. To overcome this problem
and ensure that none of the required information is removed, the best possible
solution would be to define the ROI as the combination of the regions by both
techniques.
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6. IRIS AND PUPIL LOCALISATION AND EXTRACTION
Figure 6.3: Example of the possible inaccurate results obtained from two differentIris localisation techniques. Results from approach one and two are outlined ingreen and red respectively.
Usually the undertaken approaches are similar in outcome, with minor varia-
tions. Therefore creating an overall mask, combining the two approaches reduces
the detection error and localizes the region with more precision.
Moreover, with an increase number of different approaches, the computational
time also increases, therefore in this study the results from two approaches have
been chosen to be combined. In cases where time is not of an essence, the results
from multiple techniques maybe combined for higher precision.
Another point to consider is that this procedure should be designed such that it
could be applied in the treatment stages of ophthalmology. At this stage, since the
chosen images were from open source databases the main objective has been the
localisation and extraction in a timely manner. Therefore, there is an assumption
that no eyelashes and eyelids can be viewed in the images and so their removal has
not been taken under consideration. This agrees with the treatment procedures
were the eyes are clamed open. As a result of this assumption the computational
complexity has been reduced significantly as the unwanted noise is not present
Additionally, the images for the investigation have been chosen such that they
were clear and not blurred as a result of the slight movements of the eye and the
head. Therefore filtering for deblurring was not considered further in the study.
Furthermore, an exact localisation of Iris and Pupil boundary is of interest in
this case and so no assumptions have been made in regards to their shape being
circular or elliptical. Therefore, the two chosen methodologies from the literature
would need to exactly detect the boundaries without approximating them.
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6. IRIS AND PUPIL LOCALISATION AND EXTRACTION
It should be noted that Iris and Pupil are similar in shape, so the same method-
ology with different parameters may be used to detect these features. This reduces
the complication of the implemented algorithm and so is more feasible to be used
in an automated system.
6.3 Implementation
In this section, the proposed methodology has been implemented and the results
are observed. An example, using the original image shown in Figure 6.4, has been
used for better representation of the possible outcomes.
Figure 6.4: Original image used for localisation of Iris and Pupil
The results are promising and the feature of interest has been accurately located
in comparison to the results of each of the techniques separately. Similar results
have been observed when detecting the Pupil. Example of the result obtianed
when localising the Iris and Pupil using the proposed methodology can be viewed
in Figure 6.5.
(a) Localised Iris (b) Localised Pupil
Figure 6.5: Result obtained when localising the iris and pupil outer boundariesusing the proposed new algorithm
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6. IRIS AND PUPIL LOCALISATION AND EXTRACTION
Moreover, the step by step results for Iris localisation using the proposed
methodology has been shown in Table 6.1.
Table 6.1: Example of Iris Localisation Results
Method 1 Method 2
Detection
Noise Removal
Mask
Feature Localisation
It should also be noted that majority of the pre-processing steps are the same
for different procedures, hence it is only the last stage of Iris and Pupil localisation
which may vary between the procedures. As a result the overall computational
time varies mainly due to localisation stage.
Since the two methodologies are being performed concurrently, the processing
time is also reduced in comparison to if the procedures were to be performed
separately and that is a desirable outcome for an automated process. For the
chosen methodologies the overall processing time was about 2-5 seconds.
The suggested process has been performed on over twenty different eye images.
Six samples of the obtained results for Iris localisation are displayed below in
Table 6.2.
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6. IRIS AND PUPIL LOCALISATION AND EXTRACTION
Table 6.2: Iris localisation for different images.
Original Image Overall Mask Iris Localisation
Image 1
Image 2
Image 3
Image 4
Image 5
Image 6
The results are consistent and show reasonable robustness for detection of
the Iris boundary. In cases where the pigmentation of the Iris is lighter some
misdetection is observed, such as the case in the bottom left hand corner of Image
2 in Table 6.2. This is mainly due to the error in thresholding approach where there
is less contrast between the Iris and Sclera. To overcome this problem, it is best
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6. IRIS AND PUPIL LOCALISATION AND EXTRACTION
to enhance the contrast using the suggested approach in Section 5.7. This ensures
that the contrast between the two regions are maximised and so the accuracy of
detection is improved.
6.4 Iris and Pupil Extraction
Once the Iris and Pupil have been detected and the boundaries have been localised,
the next step is to extract the feature information. The general information needed
are the center location and the area of the ROI. As a result in this section some
approaches have been suggested to obtain this information.
6.4.1 Center Decection
A simple approach has been used to estimate the location of the center of the Iris
and Macula based on the detected boundary of the localised region. To do so, the
following steps have to be undertaken:
1. Mask the ROI, so that the desired region is represented by ”1” and all sur-
rounding region are set as background and have ”0” pixel value.
2. First non-zero pixel is determined. This pixel would not be black and so
is part of the estimated Iris or Macula. It would represent the most left
non-zero pixel in the image.
3. Last non-zero pixel is then determined. This would be the last pixel of the
ROI and is located on the right hand side of the image.
4. In order to estimate the center of ROI, the horizontal and vertical pixel
locations of the first and last non-zero pixels are used to calculate the middle
point which may be considered as a preliminary location of the Iris and or
Pupil center.
5. To improve the accuracy of center estimation, the horizontal middle value
found in the previous step can then be used to determine the first and last
non-zero values in vertical directions. These points would represent the fur-
thest top and bottom points where the pixel values are still non-zero.
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6. IRIS AND PUPIL LOCALISATION AND EXTRACTION
6. Similarly using the vertical middle value found in step 4, the first and last
non-zero values in horizontal directions can be determined.
7. Once the points have been determined, their average values are taken, re-
sulting in the re-calculated center location of the Iris and Pupil. An example
of the possible results from this process, will be shown in the OD localisation
Section 8.4.1, Figure 8.6. The final center point of the ROI can be seen as
blue (+) sign on the image.
6.4.2 Area Calculation
Once the center has been localized, the next step is to calculate the area of the
ROI, in this case the Iris and Pupil. Three suggested approaches are as follow:
The first approach could be to approximate the ROI as being circular, and
use the radius (R) to detect the area (A). The radius can be calculated using the
distance between the estimated center and the four non-zero pixels found in the
suggested center localisation approach.
Once the radius has been defined, the area can be calculated using:
A = πR2 (6.1)
The second approach could be to use the perimeter (P) of the ROI to determine
its area. The perimeter can be found more accurately by considering the ROI
pixels. In a binary image of the ROI, the pixels are considered to be part of the
desired region, if they are non-zero and are connected to at least one other non-zero
pixel.
Once the perimeter is calculated, the area of the ROI can then be estimated,
since:
P = 2πR (6.2)
Therefore:
R =P
2π
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6. IRIS AND PUPIL LOCALISATION AND EXTRACTION
Substitute R back into Equation 6.2:
A = π
(P
2π
)2
=πP 2
4π2
=P 2
4π(6.3)
Using the Equation 6.3 the area of the ROI can be calculated.
Another approach used to determine the area of the ROI is to determine the
total number of the non-zero pixels. This value represents the area of the ROI
since the value of the desired region is set to ”1”, while its surrounding has ”0”
pixel value.
6.5 Summary
Iris and Pupil of the eye have been localised and their key information such as
center and area have been detected. Due to similarities in shape of both Iris and
Pupil, the proposed procedures to localise and extract these features were the
same.
In this study, the exact boundary detection, simplicity of the procedure and
the speed of detection were of interest, hence the proposed approach and the
methodologies were chosen accordingly.
Since each procedure has its own advantage and disadvantage, a new procedure
was proposed which was to obtain results from two different approaches and then
combine the outcomes to create a single mask covering both regions. The mask
could then be used to detect the ROI which in this case were the Iris and Pupil.
In this case, the thresholding and active contouring methods were selected and
their results were combined to create the mask for the ROI. Moreover, since both
methodologies were performed at the same time, the overall processing time is not
increased significantly. The obtained results for Iris and Pupil localisation proved
to be more precise, with less loss of critical information and with a reasonable
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6. IRIS AND PUPIL LOCALISATION AND EXTRACTION
computational timing.
Once the Iris and Pupil were detected, their center, radius and area were then
calculated. The location of the center was approximated by finding the middle
value in horizontal and vertical direction within the detected boundary. The ra-
dius was then calculated by measuring the distance between the center and the
boundary. Using the equation for the area of the center, the area of Iris and
Pupil were approximately calculated. For a more accurate area, the equation for
perimeter of the circle was used to calculate the area.
The outcomes have been beneficial for the fields of biometrics and optometry
as the Iris and Pupil were successfully detected and their important information
extracted.
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CHAPTER 7
RETINAL VESSELS LOCALISATION AND
EXTRACTION
Introduction Literature Review Thesis Outline Conclusion
Image Acquisition Image Pre-ProcessingFeature Localisation:
—Retinal Vessels
Feature Extraction:
— End Point
Figure 7.1: Chapter Seven Outline of Image Processing Stages
7.1 Overview
Many ophthalmological disorders influence the structure of the retinal vessels and
therefore Vasculature detection has always been one of the key areas for oph-
thalmological research. Many researchers have considered and studied the retinal
vasculature and its detection. Several of these approaches have been considered
and reviewed in Section 3.2.2.1.
Detection of exact location of vessels of the retinal image can enormously aid
medical experts in disease diagnosis and treatment. Diseases such as the ROP,
which affect the normal growth of vessels in the eye can easily be detected and
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7. RETINAL VESSELS LOCALISATION AND EXTRACTION
treated if the vessels are localized correctly and precisely. The areas which show
signs of insufficient growth can be treated, resulting in reduction of severe cases of
lifelong blindness by ROP.
Therefore, the main objectives of this study have been to locate the vascu-
lature edges and determine their end-points. The proposed methodology should
have been fast, reliable and simple. Furthermore, to reduce the computational
complication and time, the use of previously performed steps have also been con-
sidered.
7.2 Proposed Localisation Technique
Variety of different methodologies for vessel detection has been performed by re-
searchers in this field. Vessels are the most studied feature of interest in the retina
and their localisation have been covered in depth in recent years. The objective
of this research was to suggest a faster, simpler solution, so that a reasonable
outcome could be achieved as majority of the previously proposed approaches are
computationally complicated and times consuming.
To achieve this objective, the 2D FFT filtered images from Section 5.4.1 have
been chosen as the input images. Two different size kernels of several widely used
edge detector filters were then convolved with the images. The two kernels were
10×5 and 3×2 windows.
The edge detector filters, included the Sobel filter, Canny filter, Laplacian filter,
Prewitt filter, Circular Average filter, Average filter, Median filter, Weiner filter
and Gaussian filter.
Figure 7.2 depicts the flowchart of the overall procedure of the proposed tech-
nique for detection of retinal vasculature.
EyeImage
Acquisition
Image
Pre-Processing
Vessels
Localisation
2D FFT ∗ Edge Filter
Interpretation
(Feature Extraction)
End-point
Display
Figure 7.2: Proposed steps for retinal vessel localisation.
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7. RETINAL VESSELS LOCALISATION AND EXTRACTION
Canny Filter
Canny filters detect the local maxima of the gradient using the derivative of a
Gaussian filter. The strong and weak edges are found using two different thresh-
olds. Using this, it tracks the intensity discontinuities.
Sobel Filter
Sobel filters emphasize on the high spatial frequency by approximating the
absolute gradient magnitude at each point. It consists of two matrices for edge
detection in horizontal and vertical directions. The two results are then added
together to find the overall magnitude. They are:
Sobel − horizontal =
1 2 1
0 0 0
−1 −2 −1
Sobel − vertical =
−1 0 1
−2 0 2
−1 0 1
Prewitt Filter
Similar to the Sobel Filter, the following is the Prewitt filter which emphasizes
on the edges using the approximation of gradient, therefore it can be considered
as a discrete differential operator. The Prewitt filter consists of two 3×3 matrices
which are convoluted to the original image. The magnitude of the overall results
can be found by adding the outcomes from the two matrices. The two matrices
for detection of edges in horizontal and vertical direction are:
Prewitt− horizontal =
1 1 1
0 0 0
−1 −1 −1
Prewitt− vertical =
1 0 −1
1 0 −1
1 0 −1
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7. RETINAL VESSELS LOCALISATION AND EXTRACTION
Laplacian Filter
Laplacian is a second order differential operator, considering the divergence of
the gradients. The Sobel and Prewitt operator only consider the first derivative
while the Laplacian filter calculated the second derivative. In the two dimensions,
Laplacian filter is given by:
∆f =δ2f
δx2+δ2f
δy2
∇2 =4
α + 1
α4
1−α4
α4
1−α4−1 1−α
4α4
1−α4
α4
Where x and y are the Cartesian coordinates and ∇ is the divergent function.
Circular Average Filter
The Circular Average Filter is a smoothing filter which is convolved with the
image. It is capable of detecting edges using a square matrix size of 2×(radius+1),
where radius is the proposed size of the expected artefacts.
Average Filter
Another smoothing filter is the rectangular averaging linear filter commonly
known as the Average filter. In this case the value for each pixel is replaced by
the mean values of its neighbouring pixels. The process is very similar to the
convolution process. In this case a 3×3 kernel was used.
Median Filter
Median filter is a non-linear operator similar to the Average filter. In this case,
the pixel value is replaced with the median value of its neighbouring pixels using
the designated kernel size.
Weiner Filter
Weiner is a linear time-invariant filter. It minimises the mean square error
between the estimated random processes with the desired process. It is a useful
noise removal filtering approach.
Gaussian Filter
The Gaussian filter is a symmetrical low pass filter whose impulse response is
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7. RETINALVESSELSLOCALISATIONANDEXTRACTION
approximatelyaGaussianfunction.ItisdescribedbytheEquation:
hg(n1,n2)=en2
1+n22
2σ2
TheoverallGaussianfilteroftheimageisfoundusing:
h(n1,n2)=hg(n1,n2)
n1 n2
hg
wheren1andn2arethedistanceinthehorizontalandverticalaxisrespectively.
TheσisthestandarddeviationoftheGaussiandistribution.
Thesuggestedprocedureusingtheabovefiltershasbeenimplementedandthe
resultscanbeviewedinthefollowingsection.
7.3 Implementation
Theproposedprocedureforlocalisingtheretinalvesselshavebeenimplemented
andtheresultsareillustratedinTable7.1.
Table7.1: Modelingandimplementationofdifferentfiltersforvesseldetection
Filter 10×5kernel 3×2kernel
SobelFilter
CannyFilter
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7. RETINAL VESSELS LOCALISATION AND EXTRACTION
Table 7.1: Modeling and implementation of different filters for vessel detection
Filter 10 × 5 kernel 3 × 2 kernel
Laplacian Filter
Prewitt Filter
Circular Average Filter
Average Filter
Median Filter
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7. RETINAL VESSELS LOCALISATION AND EXTRACTION
Table 7.1: Modeling and implementation of different filters for vessel detection
Filter 10 × 5 kernel 3 × 2 kernel
Weiner Filter
Gaussian Filter
The results obtained from Canny filters appear to be more noisy than the
desired results. This is mainly due to the chosen thresholds. Since each image
would require its own specific threshold values, this method may not be feasible
and desirable for the purpose of this study.
Results from Sobel, Prewitt and Laplacian filters appear to be very similar.
The techniques consider variation in gradient and since the background gradient
is considered in the process, the results do not have the required clarity. The
remaining processes provided similar responses, much clearer than the Sobel, Pre-
witt, Canny and the Laplacian operations.
The results from the Circular Average filter and Average filter was very similar
due to the similarities in the process. However, under similar conditions the Av-
erage filter provided more vasculature details. Comparing the Average filter with
Median filter revealed that median values may result in more noise detection.
Based on observations, it can be said that the Average filter, Median filter,
Weiner filter and Gaussian filter revealed more details and clarity for vascular
detection.
To improve the results even further, several combinations of these filters were
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7. RETINAL VESSELS LOCALISATION AND EXTRACTION
also examined and the results have been displayed in Table 7.2.
Table 7.2: Combining results of different filters
Filter 10 × 5 kernel 3 × 2 kernel
Laplacian of Gaussian Filter
Addition of Weiner and Me-
dian Filter
Average Filter of Weiner Fil-
tered image
Weiner Filter of Median Fil-
tered image
Gaussian Filter of the
Weiner Filtered image
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7. RETINAL VESSELS LOCALISATION AND EXTRACTION
The results indicate that larger size kernel windows reveal more detail but
have more noise and unwanted error as well. In these cases the accuracy of the
procedures reduces, while the processing time increases. Therefore, for a general
overview of the vessels smaller size windows are preferred in this case.
Moreover, by combining and implementing multiple filters, the results appear
to have improved the clarity of the vasculature detection. This is quite apparent
in three cases of when an Average filter or Gaussian filter was applied to a Weiner
filtered image or when the Weiner filter was applied to the Median filtered image.
All the three cases revealed similar results, however in the case of applying Weiner
filter to the Median filtered image; it appears that some more minor details can
be viewed.
This is due to more emphasis of the locations of the vessels. In the original
results from Table 7.1, the best results for vessels localisation was found by imple-
menting a single filter to sharpen or blur the images. In this case, the emphasised
vessels are further highlighted by combining the filters and noise removal. In the
preliminary analysis, Median filter revealed more detailed structure of the vessels
while Weiner filter which is commonly used for noise removal highlighted the ves-
sels more clearly. Therefore, applying the Weiner filter to the obtained results
of the Median filter would have removed the unwanted noise and revealed more
vasculature structures. This agrees with the observations and the findings of this
study.
For confirmation of the observation, this process has been implemented on
over twenty different images. The average processing time for the retinal vessel
localisation was about 12-15 seconds. The results for five of the images has been
displayed in the following Tables 7.3 to 7.7.
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7. RETINAL VESSELS LOCALISATION AND EXTRACTION
Results for Image 1:
Table 7.3: Vessel localisation for Image 1
Filter 10 × 5 kernel 3 × 2 kernel
Original Image - 2D FFT
Filtered Image
Sobel Filter
Canny Filter
Laplacian Filter
Prewitt Horizontal Edge
Emphasizing Filter
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7. RETINAL VESSELS LOCALISATION AND EXTRACTION
Filter 10 × 5 kernel 3 × 2 kernel
Circular Average Filter
Average Filter
Median Filter
Weiner Filter
Gaussian Filter
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7. RETINAL VESSELS LOCALISATION AND EXTRACTION
Filter 10 × 5 kernel 3 × 2 kernel
Laplacian of Gaussian Filter
Addition of Weiner and Me-
dian Filter
Average Filter of Weiner Fil-
tered image
Weiner Filter of Median Fil-
tered image
Gaussian Filter of the
Weiner Filtered image
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7. RETINAL VESSELS LOCALISATION AND EXTRACTION
Results for Image 2:
Table 7.4: Vessel localisation for Image 2
Filter 10 × 5 kernel 3 × 2 kernel
Original Image - 2D FFT
Filtered Image
Sobel Filter
Canny Filter
Laplacian Filter
Prewitt Horizontal Edge
Emphasizing Filter
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7. RETINAL VESSELS LOCALISATION AND EXTRACTION
Filter 10 × 5 kernel 3 × 2 kernel
Circular Average Filter
Average Filter
Median Filter
Weiner Filter
Gaussian Filter
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7. RETINAL VESSELS LOCALISATION AND EXTRACTION
Filter 10 × 5 kernel 3 × 2 kernel
Laplacian of Gaussian Filter
Addition of Weiner and Me-
dian Filter
Average Filter of Weiner Fil-
tered image
Weiner Filter of Median Fil-
tered image
Gaussian Filter of the
Weiner Filtered image
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7. RETINAL VESSELS LOCALISATION AND EXTRACTION
Results for Image 3:
Table 7.5: Vessel localisation for Image 3
Filter 10 × 5 kernel 3 × 2 kernel
Original Image - 2D FFT
Filtered Image
Sobel Filter
Canny Filter
Laplacian Filter
Prewitt Horizontal Edge
Emphasizing Filter
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7. RETINAL VESSELS LOCALISATION AND EXTRACTION
Filter 10 × 5 kernel 3 × 2 kernel
Circular Average Filter
Average Filter
Median Filter
Weiner Filter
Gaussian Filter
143
7. RETINAL VESSELS LOCALISATION AND EXTRACTION
Filter 10 × 5 kernel 3 × 2 kernel
Laplacian of Gaussian Filter
Addition of Weiner and Me-
dian Filter
Average Filter of Weiner Fil-
tered image
Weiner Filter of Median Fil-
tered image
Gaussian Filter of the
Weiner Filtered image
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7. RETINAL VESSELS LOCALISATION AND EXTRACTION
Results for Image 4:
Table 7.6: Vessel localisation for Image 4
Filter 10 × 5 kernel 3 × 2 kernel
Original Image - 2D FFT
Filtered Image
Sobel Filter
Canny Filter
Laplacian Filter
Prewitt Horizontal Edge
Emphasizing Filter
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7. RETINAL VESSELS LOCALISATION AND EXTRACTION
Filter 10 × 5 kernel 3 × 2 kernel
Circular Average Filter
Average Filter
Median Filter
Weiner Filter
Gaussian Filter
146
7. RETINAL VESSELS LOCALISATION AND EXTRACTION
Filter 10 × 5 kernel 3 × 2 kernel
Laplacian of Gaussian Filter
Addition of Weiner and Me-
dian Filter
Average Filter of Weiner Fil-
tered image
Weiner Filter of Median Fil-
tered image
Gaussian Filter of the
Weiner Filtered image
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7. RETINAL VESSELS LOCALISATION AND EXTRACTION
Results for Image 5:
Table 7.7: Vessel localisation for Image 5
Filter 10 × 5 kernel 3 × 2 kernel
Original Image - 2D FFT
Filtered Image
Sobel Filter
Canny Filter
Laplacian Filter
Prewitt Horizontal Edge
Emphasizing Filter
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7. RETINAL VESSELS LOCALISATION AND EXTRACTION
Filter 10 × 5 kernel 3 × 2 kernel
Circular Average Filter
Average Filter
Median Filter
Weiner Filter
Gaussian Filter
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7. RETINAL VESSELS LOCALISATION AND EXTRACTION
Filter 10 × 5 kernel 3 × 2 kernel
Laplacian of Gaussian Filter
Addition of Weiner and Me-
dian Filter
Average Filter of Weiner Fil-
tered image
Weiner Filter of Median Fil-
tered image
Gaussian Filter of the
Weiner Filtered image
Studying the results, suggest that the preliminary observations were correct.
Once again the results indicated that the smaller kernel size reduced the noise,
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7. RETINAL VESSELS LOCALISATION AND EXTRACTION
while the larger kernel size showed more details. Since the noise would cause
confusion and may lead to misjudgement, the results for smaller kernel size are
preferred.
From the applied filters, the Average filter, Median filter, Weiner filter and
Gaussian filter revealed more information and displayed a better and clearer results
in comparison to the other applied filters.
Similarly, the combination of the filters once again showed that the best local-
isation of the vessels were obtained for the cases were the noise filtering Weiner
filter was applied to the Median filtered images.
7.4 Retinal Vasculature Extraction
Several key features of the vessels including its turosity and variation in diameter
have been considered in details in the literature. However, the disease which was
considered in this case was the ROP. As mentioned in Section 2.4 , ROP occurs in
premature infants and the main distinguishable feature of this disease is that the
vessels are affected as they are not well developed. The only cure for an irreversible
blindness in these infants is to apply laser treatment to the end point of affected
vessels. Therefore, in this case detecting end-point of vessels was investigated
further.
As a result, in this section, an approach has been suggested for localising the
end point of the vessels.
7.4.1 Localisation of the End Point of Vessels
In this section a method has been suggested in order to localise the end point of
the vessels.
1. Localise the retinal vessels from the fundus image.
2. Mask the vessels, so that they have the pixel value of ”1” and the remaining
background areas of the retina have the ”0” pixel value.
3. Trace the location of the vessels. If necessary burst or shrink the vessels to
the desired thickness.
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7. RETINAL VESSELS LOCALISATION AND EXTRACTION
4. Using template matching, the end point of the vessels can then be calculated.
By considering a window of 3×3, where the desired non-zero pixel is located
at the center of it, the end point can then be determined, if the middle pixel
is only surrounded by one other non-zero pixel. Else, the desired pixel may
have been located in the middle of the vessel. Figure 7.3 shows an example
of a vessels end point.
Figure 7.3: Some of the possible vessels end point using template matching
7.5 Summary
One of the most important key features of retina is the vasculature. The aim of
this chapter was to introduce a new method to localise the retinal vessels and
determine their end-points.
To reduce the processing time of the localisation procedure, the edges of the
vessels have been detected by applying multiple different filters to the 2D FFT
image which was prepared in Chapter 5. From the studied edge detection filters,
the Average and Gaussian filters applied to Weiner filter and the Weiner filter
applied to the Median filter provided the best possible vessel detection. The vessels
in these cases were clearly visible and more easily distinguishable in comparison
to the other filters and the original images. However, the best visible result was
for the case were the Weiner filter was applied to the Median filtered image.
The extracted feature of the vessels was detecting their end-points. This may
be useful in studying the vasculature growth throughout the retina and diagnosing
diseases such as ROP. Furthermore, it may also aid the ophthalmologists in treating
such diseases as the areas at risk would be highlighted. To achieve this, template
matching was applied to locate the last non-zero pixels. These pixels would only
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7. RETINAL VESSELS LOCALISATION AND EXTRACTION
have one other neighbouring non-zero-pixel and hence can be defined as the end
point of the vessels.
Successful, localisation and extraction of the vessels and their end-point were
the outcomes of this research. The short processing time of only a few seconds,
allow this process to be used in many diagnostic tools and a guide for ophthalmol-
ogists.
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CHAPTER 8
OPTIC DISK AND MACULA LOCALISATION AND
EXTRACTION
Introduction Literature Review Thesis Outline Conclusion
Image Acquisition Image Pre-Processing
Feature Localisation:
—Optic Disk
— Macula
Feature Extraction:
— Center and Radius
— Area
— Cup to Disk ratio
Figure 8.1: Chapter Eight Outline of Image Processing Stages
8.1 Overview
In the literature review chapter, some of the key features of the eye have been
identified to be important in many applications of ophthalmology, including the
OD and Macula of the eye.
Diseases such as Glaucoma are detected using the OD, which is the brightest
region in the retinal image. Glaucoma is the second leading cause of irreversible
visual loss and blindness. Hence, to minimise vision loss in patients, early detection
and treatment of Glaucoma is crucial.
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8. OPTIC DISK AND MACULA LOCALISATION AND EXTRACTION
Ophthalmologists diagnose Glaucoma by observing the visible changes which
occur at the OD. The diameter of the OD can be used as the preliminary in-
dication of the susceptibility of the patient to Glaucoma. As a result, the OD
localisation and measurement of its area are other key features of interest in the
field of ophthalmology.
Another main feature in retinal images is the macula, which is approximately a
dark circular region in the images. Macula may also help experts in their prognosis
and so its detection is important.
8.2 New Technique for Optic Disk Localisation
Over the past years, many methods have been suggested for detection of OD, each
having their own benefits and restrictions. Some of these approaches resulted in
localising OD center while others estimating its boundaries. One of such methods
has been the thresholding technique.
Thresholding technique has been widely used in the past to detect different
features of a retinal image. In this study, the thresholding approach has been used
to approximate the location of the OD. The reason being is that this approach
would provide an exact boundary of the OD in comparison to majority of the
other available techniques which assume OD to have a circular or an elliptical
shape.
EyeImage
Acquisition
Image
Pre-Processing
Optic Disk
Localisation
Histogram of ROI
Apply Threshold
/Brightest Pixels
Define OD Region
Mask OD Region
Interpretation
(Feature Extraction)
Center
Radius
Area
Cup to Disk Ratio
Display
Figure 8.2: Proposed steps for Optic Disk localisation.
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8. OPTIC DISK AND MACULA LOCALISATION AND EXTRACTION
As mentioned previously, OD appears as a circular bright spot on the fundus
retinal image. Using this characteristic, the adaptive thresholding technique has
been implemented to detect the brightest pixels of the image. Figure 8.2 depicts
the flowchart of the overall procedure of the proposed technique for detection of
Optic Disk.
In the first step, the bright pixels have been detected and the outcome has
then been displayed as a binary image. This has been achieved using automated
adaptive thresholding for each individual image. Figure 8.3, displays a sample
histogram of this process and the set threshold.
The result is somewhat noisy; therefore filters have been applied to remove this
noise. Noise removal of the result by median filtering has proven to be successful.
Figure 8.3: The gradient plot histogram used to set the threshold for the ODlocalisation.
As demonstrated in Figures 8.3 and 8.4, the OD region is defined by determin-
ing the pixels with the higher intensity values. Since not all the images have the
same intensity and brightness, the threshold has to be set individually. Defining
the threshold is easier in cases like Figures 8.4a and 8.4b as the majority of the
bright pixels are bundled together and easier distinguishable in the Gradient Plot.
However, there are times where defining this region would be more difficult such
as the case in Figure 8.4c. In such cases, the threshold has been set as the first
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8. OPTIC DISK AND MACULA LOCALISATION AND EXTRACTION
occasion where a few closely located pixels have intensity values greater than 200
gray levels.
(a) Sample 1
(b) Sample 2
(c) Sample 3
Figure 8.4: Example gradient plot histograms and set thresholds for OD localisa-tion for different images.
At this point, a reasonable outline of the OD region has become apparent
and therefore using the remaining white pixels, the center of the OD region has
been detected and an approximated boundary has been set. Depending on the
size of the OD in relation to the overall retinal image size and capturing device
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8. OPTIC DISK AND MACULA LOCALISATION AND EXTRACTION
specifications, this boundary might vary and therefore has to be set accordingly.
In this case the given boundary has a radius of 70 pixels.
Using the centre location and plotting an approximate circle a mask has been
created to locate the OD region.
Implementing the mask on to the original image has provided a more specific
region of interest. Since consecutive application of a process would enhance the
accuracy and speed of detection, at this stage the thresholding has once again been
applied. The result for the overall process has been displayed in the next section.
8.3 Implementation
In this section, the proposed consecutive adaptive thresholding technique for de-
tection of the OD has been implemented and the results have been displayed in
Table 8.1. It should be noted that for clearer visibility of the results, the images
were zoomed in.
The Adaptive thresholding method has been performed twice on the desired
image in order to accurately detect its brightest regions or in other words OD.
Table 8.1a is an example of a possible desired retinal image which has been
pre-processed according to the procedure covered in Chapter 5 and has been used
in this section for OD localisation.
Table 8.1b represents the detected brightest regions of the original image which
have the pixel values greater than 200 gray levels and are considered as the upper
region of the image histogram. It also outlays the regions which are most likely to
be the OD.
The next two rows, Table 8.1c and 8.1d represent the regions which are having
the pixel values greater than the calculated mean value and the minimum value of
the upper region respectively.
Combining the findings would result in detection of the possible center of the
OD region and is shown in Table 8.1e.
Once the center has been localised, a boundary is set and plotted to the region
which is most likely to be the location of the OD. This has been displayed in
Table 8.1f.
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8. OPTIC DISK AND MACULA LOCALISATION AND EXTRACTION
Table 8.1: Step by step results for OD detection, applying the proposed consecutiveadaptive thresholding method.
STAGE 1
(a) Original image
(b)Detected lighter region with pixel values greater
than 200 gray level
(c)
Detected lighter region using pixels with values
greater than the mean value from the upper region
of the histogram
(d)
Detected lighter region using the pixels with val-
ues greater than the minimum value from the up-
per region of the histogram
(e)Detected center of OD region using results from
part(d)
(f) Outline of the OD region
STAGE 2
(g)Cropped OD region using results from the first
cycle
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8. OPTIC DISK AND MACULA LOCALISATION AND EXTRACTION
Interested Region Outcome
(h)Detected lighter region with pixel values greater
than 200 gray level outlined in blue
(i)
Detected OD using the pixels with values greater
than the minimum value from the upper region of
the histogram outlined in green
(j)
Detected OD using the pixels with values greater
than the mean value from the middle Region of
the histogram outlined in red
(k)Detected OD by illustrating results from sections
(h), (i) and (j).
The possible OD region has now been determined. In order to segment the OD
region in the original retinal image, a mask has been created and implemented and
the outcome is shown in Table 8.1g. At this point a second round of the adaptive
thresholding procedure has been applied.
Similar to the previous round, the Table 8.1h, 8.1i and 8.1j represent the de-
tected lighter regions with pixel values greater than 200 gray levels, minimum value
of the upper region and mean value of the middle region accordingly.
Combing the results and plotting boundaries around the detected regions, out-
lines the possible location of the OD. The final result is illustrated in Table 8.1k.
This automated process has proven to be successful in localising the exact
boundary of the brightest region of the retinal image, which is considered as the
OD. It has been implemented on more than twenty different images. The re-
sults obtained using this exact OD detection methodology appears to be of higher
precision in comparison to the other available procedures, with an average com-
putational time of 20-25 seconds.
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8. OPTIC DISK AND MACULA LOCALISATION AND EXTRACTION
Table 8.2: OD localisation for different images.
Original
Image
Pixels > min-
imum value of
upper region
Pixels >
mean value of
middle region
OD
Image 1
Image 2
Image 3
Image 4
Image 5
Table 8.2 is the results of five different images from the studies database, illus-
trating the OD localisation using the proposed Consecutive Adaptive Thresholding
technique. The results show that the OD localisation has been successful for all
cases and the OD boundaries have been exactly detected.
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8. OPTIC DISK AND MACULA LOCALISATION AND EXTRACTION
8.4 Optic Disk Extraction
Once the OD has been localized, the next step is to extract the necessary needed
information from the detected ROI.
During disease diagnosis and its progression, the ophthalmologists look at the
shape of the OD and its variation in diameter. Therefore, estimation of the OD
center is the first necessary step in extracting the information. This is then followed
by calculation of its area and later on determing the cup to disk ratio.
8.4.1 Center of the Optic Disk
Two approaches have been suggested by the author, in order to estimate the
location of the center of the OD.
The first method is to estimate the location of the center of the OD based
on the detected boundary of the localised OD. This is similar to the previously
suggested center calculation for Iris and Pupil in Section 6.4.1 of this chapter.
The second method is to determine the location of the center based on the
originating of the vessels within the OD. In this case the use of Template Matching
has been suggested.
1. Extract the OD from the retinal image.
2. Localize the vessels within the OD using the methodology suggested in sec-
tion 7.1 of this chapter.
3. Mask the vessels, so that the vessels have the pixel value of 1 and the re-
maining areas of the OD have the 0 pixel value.
4. Trace the location of the vessels. If necessary burst or shrink the vessels to
the desired thickness.
5. Calculate the point of intersection by implementing template matching con-
cept, determining whether the surrounding pixels of a middle value pixel in
a 3×3 window is zero or one. If the middle pixel in red is our desired pixel
and it is surrounded with at least three other non-zero pixels as shown in
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8. OPTIC DISK AND MACULA LOCALISATION AND EXTRACTION
Figure 8.5, it can be said that intersection has occurred at the desired pixel.
Otherwise, it can be the middle or an end point in the vessel.
Figure 8.5: Some possible templates for determining vessels intersection
6. The point of intersection, represents the origination of the vessels and so the
center of the OD.
Example of the result obtained implementing the suggested methodologies to
detect the center of the OD can be viewed in Figure 8.6.
(a) Detected OD center
;(b) Zoomed in image
;
Figure 8.6: Center localisation of the OD, method 1 is represented as a blue (+)sign and method 2 as red (+) sign
Comparing the two methodologies, the results are approximately similar. In
majority of the cases, the first methodology is sufficient, unless otherwise the
location of the origination of the vasculature is also on importance in disease
detection and prognosis. The overall processing time is about 1-2 seconds and this
is due to simplicity of the process and reduction in the size of the ROI by confining
it to the OD region.
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8. OPTIC DISK AND MACULA LOCALISATION AND EXTRACTION
8.4.2 Area of the Optic Disk
Area of the OD can be calculated following the same procedure which was used
previously in Section 6.4.2 for determining the area of the Iris and Pupil.
8.4.3 Cup to Disk Ratio
For diagnosis of diseases such as Glaucoma, ophthalmologists would consider the
area and variation in the shape of the OD, as well as the cup to disk ratio [46, 157,
158, 159]. In the previous sections, the area and the overall shape of the OD has
been detected and analysed. In this section, a suggestion has been made to detect
the Optic Cup (OC) so that it could be used to determine the cup to disk ratio.
Detection of the OC which outlines the borders of the Optic Nerve Head (ONH)
is quite difficult in comparison to the OD localisation as it may not clearly be visible
in the fundus image. On the coloured retinal images, it usually appears as a pink
colour or change in contour from rim to the cup [160].
Although, it may not be possible to accurately detect the OC in all the images
as it may not be visible, in this study it has been suggested to detect the OC using
the similar approach as the suggested consecutive adaptive thresholding which was
used for OD localisation. The overall procedure would be similar but performed
on contrast enhanced images. Since the contrast of the images has changed, the
automatically detected threshold value would also defer, resulting in detection of
the OC. Enhancing the contrast would help in distinguishing and detection of the
cup boundary. More details on how to enhance the contrast of retinal images is to
be covered in Chapter 5.7.
Figure 8.7: Detection of the OC (green) and OD (red)
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8. OPTIC DISK AND MACULA LOCALISATION AND EXTRACTION
Figure 8.7 represents a sample result which have been obtained when imple-
menting the procedure to detect the OC and OD. As it can be seen, the OC has
been detected and the boundary is shown in green, while the detected OD has
been shown in red.
Once the cup has been localized, similar feature extraction procedures as those
for OD can be performed in order to determine the radius and area of the cup.
Using the obtained area, the ratio between the OD and OC can then be calculated
and used by ophthalmologists for determining the rate of progression of diseases.
The common approach is to visually examine the symmetrical and shape of the OD
and OC under the slit lap biomicroscopy. In the case of Glaucoma, based on the
study performed by Nicolela [160], the cup to disk ratio asymmetry of 0.2 or greater
between the fellow eyes of the patient can be suggestive of this disease.. Therefore,
with the aid of the suggested approach it is possible to help ophthalmologists with
their diagnosis.
8.5 Macula Localisation - Proposed Technique
In order to detect the Macula, its visual characteristics have to be defined. Based
on the definition mentioned in section 2.1.2, Macula is a darkly pigmented circular
region near the center of the fundus retinal images and its structures are responsible
for high acuity vision.
(a) (b) (c) (d)
Figure 8.8: Different positions of macula in retinal images, in images (a) and (d)macula is located in the center while in images (b) and (c) no macula is present.The macula has been manually defined and can be viewed in the images.
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8. OPTIC DISK AND MACULA LOCALISATION AND EXTRACTION
From observing a number of fundus images, it can be seen that macula may or
may not be present in the image of interest, as shown in Figure 8.8. In Figures 8.8b
and 8.8c, no Macula is visible because of the angle of the image. It can also be
seen that depending on the location of the OD, macula may approximately be
localized as well.
Therefore in this study, prior to localisation of macula, it has been suggested
to initially locate the OD. This is then followed by defining whether Macula is
expected to be present or not. In cases where the Macula is not expected to be
present, further processing is not necessary. However, in cases where the Macula
is expected to be present, process proceeds and macula is localised using Neural
Network (NN) concept.
Figure 8.9 illustrates the flowchart of the overall procedure of the proposed
technique by the author for detection of Macula.
EyeImage
Acquisition
Image
Pre-Processing
Macula
Localisation
OD Coordinates
Is Macula Present?
Complement Image
Adaptive Thresholding
No Further Analysis
Interpretation
(Feature Extraction)Display
Center
Radius
AreaNoYes
Figure 8.9: Proposed steps for Macula localisation.
Neural Networks has been widely used in different areas. In ophthalmology, it
has mainly been used in detection of vessels in retinal images [161]. However in
this study, the concept of NN has been used to determine whether Macula is or
is not present in the given retinal image. If it is present, the Macula can then be
localised.
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8. OPTIC DISK AND MACULA LOCALISATION AND EXTRACTION
To do so, the fundus image has been segmented in to blocks. 9 blocks in this
case. The number of blocks depends on the ratio and the size of OD in relation
to the size of the retinal image. Based on the observation, for the set of analysed
image in this case, 9 blocks have been sufficient and resulted in accurate macula
localisation. In other cases, in which the image or the capturing instrumentation
specification may vary, the number of blocks may also vary. Figure 8.10 illustrates
a sample of retinal image being separated into the desired number of blocks.
Figure 8.10: The retinal image has been deperated into blocks.
Using the simple feed-forward concept of the NN depicted in Figure 8.11, each
block is considered as the input. The inputs are then checked for the presence
of the OD with them. If OD is present, the output would be set as 1, otherwise
it would be set to 0. It should be noted that the weight for each input block is
the same since OD may be present in any of the blocks. Once the block in which
contains the OD is determined, the blocks which are most likely to contain the
Macula are then investigated further.
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8. OPTIC DISK AND MACULA LOCALISATION AND EXTRACTION
Input Layer Hidden Layer Output Layer
Input 1
Input 2
Input 3
Input 4
Input 5
Input 6
Input 7
Input 8
Input 9
OD
No OD Ouput
Figure 8.11: Neural network model determining the OD block.
Based on observations, the OD is normally located in the centre, sides or diag-
onals of the images. Depending on the number of blocks and the location of the
OD, it is then possible to estimate the location of the Macula.
Moreover, on average Macula is approximately located 3 mm temporal to the
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8. OPTIC DISK AND MACULA LOCALISATION AND EXTRACTION
OD [162]. Therefore, on images where OD is located on the right or left hand
side, Macula is most likely located in the center of the image. However, this is still
dependant on the magnification and the angle in which the image is taken from.
If the instrument or possible output of the retinal fundus image is unknown,
it is best to implement Macula detection process to all the blocks expect those
of the background and the OD. This would ensure that the macula is detected
irrespective of the possible estimated ROI.
OD has been previously detected in Section 8.1. To detect Macula, it is impor-
tant to determine in which of the created blocks the Macula is more likely to be
present. For example in Figure 8.10, since the OD is located on the left hand side
in block (4), the Macula is most likely be present in center of the image in Block
(5) or with a lower probability on the right hand side in block (6). Therefore, the
localisation process may only be applied to these two blocks.
There are also possible cases where the OD is not present. In such cases the
Macula localisation may proceed throughout all the blocks.
In other cases where the OD is present in the middle block (block 5), the Macula
may or may not be apparent in the image and therefore the Macula localisation
procedure has to be implemented to all the blocks. However, there is a possibility
that the Macula is covered by the OD and may not be visible.
Once the possible blocks for which the Macula is most likely to be presented in
has been defined, the processing steps similar to those previously used for OD lo-
calisation can be implemented. However, there is a slight alteration to the method-
ology.
Since the Macula is a dark circular region of the retina, the darkest pixels
have to be located instead of the brightest pixels which have been previously
selected in the case of the OD. Another option which was implemented in this
study would be to obtain the complementary image, in which the brightest pixels
correspond to the darkest pixels of the original image or vice versa. An example of
the complementary image can be viewed in Figure 8.12. Once the complementary
image is obtained, the localisation process would be exactly as it was for the case
of the OD.
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8. OPTIC DISK AND MACULA LOCALISATION AND EXTRACTION
(a) Image (b) Complement
Figure 8.12: Complementary image. (a) Original Image, (b) Complement Image.
Advantages of using the proposed technique are that the error in localisation
of the Macula is reduced significantly. The error is reduced as the first step is to
deter ermine the presence of the macula. If this step is not included the macula
may be located wrongly. Moreover, since the desired ROI is reduced in size the
overall processing time has also reduced. Detection of both Macula and OD may
also be helpful in more accurate formation of fundus maps, which was discussed
in chapter 3, as these features can also be used as markers similar to the vessels
locations.
8.6 Implementation
The proposed methodology for Macula localisation has been performed and the
outcome can be viewed in Figure 8.13. The results are promising and the approx-
imate detection of the Macula has been a success.
(a) Image (b) Detected Macula
Figure 8.13: Localisation of Macula using the proposed technique.
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8. OPTIC DISK AND MACULA LOCALISATION AND EXTRACTION
Table 8.3: Macula localisation for different images. For cases where the Maculacannot be seen the process is stopped, such as the case for Image 6.
Image Complement Macula Region Macula
Image 1
Image 2
Image 3
Image 4
Image 5
Image 6Macula not vis-
ible
Macula not vis-
ible
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8. OPTIC DISK AND MACULA LOCALISATION AND EXTRACTION
Observing the results suggests that Macula detection using the proposed tech-
nique has been successful. This process has been implemented on over twenty
images from the used databases with an average processing time of about 3-5
seconds, some of the results have been illustrated in Table 8.3.
8.7 Macula Extraction
Macula is considered to be circular in shape, similar to the OD. Therefore, the
important information which may need to be extracted from the Macula is the
center and the radius. As a result, the approaches undertaken to estimate these
information are the same as what was previously suggested for OD extraction.
Since in this case, the origination of the blood vessels were not of interest and
so the chosen process for locating the center of macula was similar to the one
suggested in Section 6.4.1.
The approach for calculating the area of the macula was also similar to the
suggested method in Section 6.4.2.
The approximate processing time for detection of the center and area of the
macula was less than 1 second, which suggests a very fast processing time due to
simplicity of the suggested procedures.
8.8 Summary
Localisation and extraction of OD and Macula has significant impact in ophthal-
mology as some of the widely affecting diseases such as Glaucoma affects these
features. Therefore variation in shape of OD and Macula can be useful in an early
detection of these diseases.
This chapter looked into the possibility of extracting information from these
features via their accurate localisation. A new method of Consecutive Adaptive
Thresholding technique has been introduced for finding the brightest pixels in
the image in order to exactly outline the OD boundary with a high accuracy.
Possibility of detecting OC has also been suggested at this section, as knowing
the ration between the OD and OC is used to determine the possibility of the
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8. OPTIC DISK AND MACULA LOCALISATION AND EXTRACTION
occurrence of a disease.
Similarly Macula has also been localised using the same technique. However,
since Macula is the darker region of the retina, the process was altered to some
extent. The Consecutive Adaptive Thresholding approach in this case was used to
detect the darker region of the retina on the complement image. Moreover, there
are times where Macula is not visible in the image as it is over shadowed by the
OD. Hence, prior to the implementation of the technique, Neural Network concept
was applied to determine whether Macula was present or not. If it was present,
then the procedure was performed.
To extract information from the localised OD and Macula, their center was
initially detected. This was then followed by radius and area of the two regions.
In the case of the OD, the ration of the OD to OC was also determined as the
determining factor of occurrence of diseases such as Glaucoma.
The proposed new approach was able to accurately locate the OD and Macula.
The exact boundary detection instead of circular assumption was performed in
order to enhance the accuracy of the extracted information further. The extracted
features were also calculated in order to help ophthalmologists in their diagnosis.
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CHAPTER 9
CONCLUSIONS
Introduction Literature Review Thesis Outline Conclusion
Figure 9.1: Chapter Nine Outline
9.1 Overall Research Program
Ophthalmology has been a growing field in the recent years. With the aid of
the new medical instrumentations and Telemedical devices, ophthalmologists have
been able to diagnose, treat and monitor patients.
The most important stage for treatment of any condition is its early detection.
To aid the ophthalmologists in the diagnosis stage, this study concentrated on
some of the most widely affecting disease such as Cataract, Glaucoma, and ROP.
For each of their key descriptors and features; Iris and Pupil, OD and Macula and
retinal vessels; image processing techniques were suggested for their localisation
and examination. Furthermore, the study was designed such that it could be
used as part of a Telediagnostic tool, which could also be used in rural areas and
developing regions where the availability of resources and expertise are limited.
To achieve this objective, improvements and modifications for all stages of
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9. CONCLUSIONS
image processing, including image acquisition, pre-processing, feature localisation
and extraction were suggested. The considerations for the proposed techniques
were the simplicity, robustness, fast processing time and high accuracy, with min-
imal user input. The processes were designed such that all the obtained results
could be stored onsite or transferred offsite to be used by ophthalmologists for
their prognosis.
9.2 Research Findings, Perceived Contributions
In this study, each chapter has concentrated on a specific stage of image processing
and some modifications were suggested for each stage. The proposed techniques
were fast, reliable, non-invasive and with a reasonable accuracy.
In order to examine and study a problem, data is required. Therefore, the
first step of image processing is image acquisition. For the purpose of this study,
open source data bases were chosen so that the compatibility of the procedures on
different input data could be monitored and examined. Several different databases
were considered including STARE, DRIVE, MESSIDOR, REVIEW, ROC, CMIF
and UPOL. To study the Iris and Pupil, the images from UPOL database were
chosen. For the cases were retinal fundus images were required, the STARE and
DRIVE data bases were chosen because they are the most widely used databases
by researchers in this field were chosen. All the consecutive steps in this study
were performed on over twenty different images from these databases.
Due to limitations, accessibility and cost of instrumentations in remote loca-
tions, majority of developing nations may only have access to minimal resources.
As a result, to create a wider view on the retina, the use of multiple markers and
images was suggested in order to create a fundus map using normal view angle
cameras. Ophthalmologists can then use this map to diagnose and treat diseases.
The suggested methodology for creating the fundus map used geometric charac-
teristics of the images and included overlapping regions with more markers. As a
result, significant amount of unwanted duplicate noise was removed.
The next a crucial step in image processing is image pre-processing. Different
stages including the colour separation, segmentation and masking of the ROI, noise
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9. CONCLUSIONS
removal of the images using 2D FFT filters and sharpening them were considered
and examined. These steps were performed in order to prepare the images for the
consecutive stages, their main objective is to remove all the unwanted information
and as a result reduce the overall processing time.
Moreover, studying the results indicated that in some special cases further
image pre-processing stages may be required. Two of such circumstances were
studied as part of this research.
Observing the results indicated that there were times where the bright fringe
noises affected the detection precision, especially in the case of OD localisation.
As a result, circular and elliptical trimming was suggested to be implemented
prior to feature localisation, in the pre-processing stage. After this application,
the precision of results performed was greatly improved to 100% success rate in
comparison to the other previously suggested procedure in the literature.
In other cases, the accuracy of the results was affected due to the contrast
of the images. This specially became apparent when thresholding technique was
considered. In such cases, the contrasts of the images were enhanced using the
Intensity Adjusted, Histogram Equalization and Adaptive Histogram Equalization.
The next two main stages of image processing included the feature localisation
and extraction. As mentioned previously, the main key features considered in this
study for diagnosing Cataract, ROP and Glaucoma were the Iris and Pupil, retinal
vessels, OD and Macula respectively.
For detection of the Iris and Pupil boundaries, which is beneficial for Cataract
diagnosis and Biometrics application, an amalgamates procedure was suggested to
incorporate and combine the results from two or more different processes in order to
create a single outlay to mask and segment the ROI. By combining two different
techniques of Thresholding and Active Contouring, the suggested methodology
has improved the accuracy of the detection in about 2-5 seconds. Since the two
procedures occur simultaneously the processing time is not increased significantly
while the results are of higher precision. To quantize the chosen region approaches
were suggested to approximately determine the center of the Iris and Pupil and
then calculate their areas.
Using fundus images, the retinal vasculatures were examined and localised for
diagnosis of diseases such as ROP, where the complication affects the vascular for-
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9. CONCLUSIONS
mation and shape. To do so, two different size kernels of several filters were applied
to the readily available 2D FFT filtered images from the pre-processing stage. The
applied filters included the Sobel, Canny, Laplacian, Prewitt, Circular Average,
Average, Median, Weiner and Gaussian filters. The observed results suggest that
larger size kernels revealed more information, but had more unwanted noise as
well. From the implemented sample filters, four revealed more details. The filters
were the Average filter, Median filter, Weiner filter and Gaussian filter. However,
there was still noise present in the outcomes; hence a combination of them was
studied. Three of the results showed very clear vasculature edges, including the
application of Average filter or Gaussian filter on the Weiner filtered image and
Wiener filter when it was applied to the Median filtered image. There were some
slight variation between the three best results but by observation, it could be said
that when Weiner filter was applied on a median filtered image, some more details
could be viewed in the output. To analyse the findings it was suggested to de-
termine the end-point of the vessels using template matching. The simplicity and
the reasonable processing time of about 12-15 seconds for the suggested vascular
localisation process makes it a suitable preliminary telemedicine tool for determin-
ing the high risked patients who might suffer from retinal vascular disorders such
as ROP.
Lastly, OD and Macula are used to diagnose and monitor the progression of
Glaucoma. To localise the boundary of OD, a new iterative thresholding method-
ology was suggested. On the contrary to the majority of the available OD approx-
imation localisation techniques, this method determines the exact OD location
and shape. The variations and changes to the OD shape were also examined by
obtaining its center, area and cup to disk ratio. The overall processing time for
OD localisation was about 20-25 seconds.
Macula was examined using a similar approach as to the OD. Firstly, the
retinal image is checked for the visibility of the Macula using the Neural Network
concept. If the Macula was visible, the thresholding approach was applied to the
complement of the image, localising the Macula in 3-5 seconds. Center and area
of the Macula were also calculated.
This study indicated that if need be all the main key features for critical wide
spread diseases may be localised and monitored in under a minute. The simplicity
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9. CONCLUSIONS
and robustness of the chosen approach also ensured that it could be used as part
of a Telediagnositc tool.
9.3 Proposals for Future Research
Research is an ongoing field and with advancements in technology, current available
approaches may further improve. In this section, a few suggestions have been given
by the author as the possible future work.
• Larger databases
– The databases used in this study were limited to the online available
open source links. However, by creating a larger database not only
the accuracy of detection for a specific disease increases but also wider
range of ailments could be detected using disease maching.
• Considering other diseases
– There are times when irreversible damage may be caused when critical
information is missed by the medical practitioner. This may be due to
the limited expertise of the ophthalmologists, rareness of a disease or
patient suffering from several medical conditions. In such cases, looking
into a larger database, covering many other diseases can be of great
assistance. To do so, many other studies on different diseases should
be performed so that the overlapping information and features of the
ailments could be defined and a larger database formed. Using this
database, then the medical practitioner could pinpoint and determine
what the main cause of the condition.
– One of the diseases which affect a wide population is Primary Angle
Closure Glaucoma (PACG). PACG causes development of angle closure.
The narrow angle is treated using the laser periphery iridotomy, if de-
tected early. Currently the Ultrasound Bio-Microscopy (UBM) is used
to detect the narrow angles, but since the procedure involves immersion
178
9. CONCLUSIONS
of the eye into fluid, it is time consuming, inadequate and inconvenient
as a routine test. To improve the testing procedure, a study can be per-
formed incorporating results from the Optical Coherence Tomography
(OCT) and the Ultrasound Bio-Microscopy (UBM). Since both OCT
and UBM are capable of obtaining cross sectional images of the Ante-
rior Chamber (AC), they may provide a better potential information in
detection of patients who might be at risk of angle closure.
• Considering other features
– In the image acquisition stage of the study, the effect of light and its
refraction was considered when passing through the eye and creating the
retinal images. The results suggested that there is a significant different
between the incident and refractive light rays which is usually ignored
and is not considered when analysing results. In order to consider this
effect, further calculation in consecutive steps of processing is needed
for determining the exact locations of the key features of the eye.
– Localization of other features and conditions can further help in cre-
ating a broader database. An example could be including results from
detection of microaneurysm [163].
– As mentioned previously, main feature for detection of the occurrence
of the complication during cataract surgery is variation in colour of
the eye. Using the colour index and its variation can therefore help in
detection of such complications.
– For the case of ROP, since the outgrowths of vessels are of great impor-
tance, the use of fractal approach can help in estimating the angiogene-
sis growth. This information can be included and used by the surgeons
to oversee the progression of the disease.
179
9. CONCLUSIONS
• Combining other results
– Patients Records
∗ The obtained results may also benefit if other information from the
patient such as ocular pressure could be available. For example if
variation of ocular pressure could be constantly monitored during
the cataract surgery, any changes in pressure may assist the pro-
posed monitoring system and alert the surgeons of the possibility
of complication.
– Results from other devices
∗ In this study, image processing methodologies were of main inter-
est. Incorporating the information and results from other medical
devices such as the OCT [164], fluorescence angiograms, use of in-
frared lighting with the results obtained from this study can aid
the ophthalmologists to make a more valid and reliable decision in
their disease prognosis.
∗ Real time feedback from the OCT can also aid the cataract surgery
significantly as the thickness of the posterior capsule can be con-
tinuously measured intra-operatively. Any changes in the thickness
can then alert the surgeon. This may also help in creating the 3D
view of the eye during the surgery as the location of the device, the
depth of the eye and all its features can be calculated and defined.
∗ Including results obtained from Confocal Scanning Laser Tomogra-
phy (CSLT) which is widely used for three dimensional scanning of
the ONH would provide a better insight into the extent of progres-
sion of Glaucoma. However, further statistical examination of the
progression of the structural glaucomatous damage as well as im-
provements on the repeatability of the images obtained using this
technique is required.
To do so, a Statistic Image Mapping (SIM) can be performed which
may benefit the field of neuro-imaging. The active changes of the
ONH can be visualised by applying the pixel by pixel analysis of
180
9. CONCLUSIONS
the topographic height over time. The flagged change map and
the intensity variation can be used to determine active changes
of the ONH and determine the progression of the disease. The
repeatability of the images can be tested by comparing the findings
with the results obtained from the Topographic Change Analysis
(TCA) system.
• Improvements of devices
– Hardware improvements - For instance in the cataract surgery case,
placing sensors on the head of the phacoemulsification handheld device,
in order to measure the input and output flow can help in constant
monitoring of the intraocular pressure and so automatically stoping the
surgery if any irregularity is seen.
– Improvements on portable handheld capturing devices - With increase
in technology and its availability in remote locations, image processing
can further enhance. Capturing high quality retinal images using mo-
bile phones are the next step in disease classification. Despite several
studies being recently conducted in this field, it may still acquire im-
plementation of several new filtering systems and image enhancement
mechanisms.
– Improvements of OCT -
∗ Create a real time, high speed anterior segment OCT system which
can quantitatively analyse the angle parameters. The designed
OCT should use the 1.3µm light source instead of the 0.8µm light
source which would provide better visualisation of the features and
enhance the speed of processing significantly faster than the current
available segment OCT systems. This is due to the lower scattering
of light at this wavelength as well as about 90% reduction of light
incident reaching the retina as it is absorbed by the water in the
ocular media. This system may be applied to analyse the angle
parameters, which can then be used for narrow angle detection and
diagnosing diseases such as PACG.
181
9. CONCLUSIONS
∗ Incorporation of the ultra-board spectral bandwidth light sources
in order to reduce the cost and enhance the axial resolution of
OCT production. The OCT technology may further benefit from
combining the outputs of other available technologies such as the
Retinal Thickness Analysis (RTA), Heidelberg Retinal Tomograph
(HRT) and Scanning Laser Polarimetry which are also capable of
determining the retinal thickness and the Retinal Nerve Fiber Layer
(RNFL) thickness. The combination and advancements in this area
may benefit the data acquisition and abnormality detection for dis-
ease diagnosis. There are many challenges involved in progression
of this technology and therefore further detailed examination is re-
quired.
These were a few possible further improvements on the current available tech-
niques. This suggests that there are many other aspects in ophthalmology which
need further attention and research. The use of biomedical applications can cer-
tainly meet these needs in conjunction with advancements in technology.
182
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APPENDIX A
Table ?? was used to plot the Figure 4.9. It was calculated using the Equation 4.12
indicating the relation between the index value of the angle of incidence and re-
fraction when light passes through two different materials.
n1sin(θ1) = n2sin(θ2) (1)
Table 1: Angle of light as it enters the eye (Incident Ray), passes through differentinterfaces within the eye and reaches the back of the eye (Refractive Ray).
Incident
Ray
(Degrees)
First
Interface
(Radians)
Second
Interface
(Radians)
Third
Interface
(Radians)
Fourth
Interface
(Radians)
Refractive
Ray
(Degrees)
0 0.00 0.00 0.00 0.00 0.00
5 0.07 0.06 0.06 0.07 3.74
10 0.13 0.13 0.12 0.13 7.46
15 0.19 0.19 0.18 0.19 11.16
20 0.26 0.25 0.24 0.26 14.82
25 0.32 0.30 0.30 0.32 18.43
30 0.37 0.36 0.36 0.37 21.96
35 0.43 0.41 0.41 0.43 25.40
40 0.48 0.46 0.46 0.48 28.74
45 0.53 0.51 0.50 0.53 31.93
50 0.57 0.55 0.54 0.57 34.96
200
. APPENDIX A
Incident
Ray
(Degrees)
First
Interface
(Radians)
Second
Interface
(Radians)
Third
Interface
(Radians)
Fourth
Interface
(Radians)
Refractive
Ray
(Degrees)
55 0.61 0.59 0.58 0.61 37.78
60 0.65 0.62 0.62 0.65 40.37
65 0.68 0.65 0.64 0.68 42.68
70 0.70 0.68 0.67 0.70 44.66
75 0.72 0.70 0.69 0.72 46.26
80 0.74 0.71 0.70 0.74 47.44
85 0.75 0.72 0.71 0.75 48.17
90 0.75 0.72 0.71 0.75 48.41
95 0.75 0.72 0.71 0.75 131.83
100 0.74 0.71 0.70 0.74 132.56
105 0.72 0.70 0.69 0.72 133.74
110 0.70 0.68 0.67 0.70 135.34
115 0.68 0.65 0.64 0.68 137.32
120 0.65 0.62 0.62 0.65 139.63
125 0.61 0.59 0.58 0.61 142.22
130 0.57 0.55 0.54 0.57 145.04
135 0.53 0.51 0.50 0.53 148.07
140 0.48 0.46 0.46 0.48 151.26
145 0.43 0.41 0.41 0.43 154.60
150 0.37 0.36 0.36 0.37 158.04
155 0.32 0.30 0.30 0.32 161.57
160 0.26 0.25 0.24 0.26 165.18
165 0.19 0.19 0.18 0.19 168.84
170 0.13 0.13 0.12 0.13 172.54
175 0.07 0.06 0.06 0.07 176.26
180 0.00 0.00 0.00 0.00 180.00
201
APPENDIX B
Following are the results obtained from conversion of the coloured images to their
corresponding gray scaled and indexed images.
Table 2: Gray Scaled and colour component separation of coloured images
Image Original Gray Scaled Red Band Green Band Blue Band
1
2
3
4
202
. APPENDIX B
Image Original Gray Scaled Red Band Green Band Blue Band
5
6
7
8
9
10
11
12
203
. APPENDIX B
Image Original Gray Scaled Red Band Green Band Blue Band
13
14
15
16
17
18
19
20
204
APPENDIX C
Following are the results obtained for the suggested approach in creating individual
masks for different images using the thresholding technique.
Table 3: Masks created for different images using Thresholding technique
ImageCreated
Mask
Masked Im-
ageImage
Created
Mask
Masked Im-
age
1 2
3 4
5 6
7 8
205
. APPENDIX C
ImageCreated
Mask
Masked Im-
ageImage
Created
Mask
Masked Im-
age
9 10
11 12
13 14
15 16
17 18
19 20
206
APPENDIX D
Following are the results obtained by implementing the 2D FFT filter.
Table 4: 2D FFT filtered images.
Image Gray Scaled Magnitude and Phase Plot Filtered
1
2
3
4
207
. APPENDIX D
Image Gray Scaled Magnitude and Phase Plot Filtered
5
6
7
8
9
10
11
12
208
. APPENDIX D
Image Gray Scaled Magnitude and Phase Plot Filtered
13
14
15
16
17
18
19
20
209
APPENDIX E
Following are the results obtained for sharpening the retinal images.
Table 5: Sharpening the retinal images using 2D FFT filtered images.
Image Gray Scaled FilteredSharpened-
10×5 kernel
Sharpened-
3×2 kernel
1
2
3
4
210
. APPENDIX E
Image Gray Scaled FilteredSharpened-
10×5 kernel
Sharpened-
3×2 kernel
5
6
7
8
9
10
11
211
. APPENDIX E
Image Gray Scaled FilteredSharpened-
10×5 kernel
Sharpened-
3×2 kernel
12
13
14
15
16
17
18
212
. APPENDIX E
Image Gray Scaled FilteredSharpened-
10×5 kernel
Sharpened-
3×2 kernel
19
20
213
PLEASE NOTE
The following materials cannot be reproduced online and have been extracted: Ektesabi, A & Kapoor, A 2011, 'Exact pupil and iris boundary detection,' Control, Instrumentation and Automation (ICCIA), 2011 2nd International Conference on, 1217-1221. DOI: 10.1109/ICCIAutom.2011.6356835 Ektesabi, A & Kapoor, A 2012, 'Complication prevention of posterior capsular rupture using image processing techniques,' Proceedings of the World Congress on Engineering, 603-607. www.iaeng.org/publication/WCE2012/WCE2012_pp603-607.pdf Ektesabi, A & Kapoor, A 2014, 'Removal of Circular Edge Noise of Retinal Fundus Images,' Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV). http://world-comp.org/preproc2014/IPC3384.pdf