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Retinal Image acquisition from Ophthalmologist
Result comparison with that of Ophthalmologist
Image Processing and Automation algorithm in
MATLAB
Start
Stop
PROJECT
FLOWCHART
August 03,2010 2Confidential Information
What is Diabetic Retinopathy(DR)?
It hampers normal vision ability by causing the
person to see black patches High blood sugar level damages retinal vessels
causing DR Silent disease in initial stage
Stages of DR Microaneurysms(MA) Exudates
Hard exudates Soft exudates
HemorrhagesAugust 03,2010 3Confidential Information
Microaneurysms First unequivocal signs of DR Tiny dilations of capillaries Appear as reddish brown spots in retinal fundus images They increase in number as the degree of retinal involvement progresses Play a key role in mass-screening and monitoring of
DR
August 03,2010 4Confidential Information
Retinal Image acquisition from Ophthalmologist
Result comparison with that of Ophthalmologist
Image Processing and Automation algorithm in
MATLAB
Start
Stop
PROJECT
FLOWCHART
August 03,2010 5Confidential Information
Image
Processing and
Automation
algorithm
Flowchart
Stop
Detection of Microaneurysms using Bottom Hat transform and Morphological operators
Identification of Blood Vessels using Bottom Hat transform
Identification of Optic Disk using Hough Transform
Retinal Image acquisition
Retinal Image plane separation
Start
August 03,2010 6Confidential Information
Retinal Image Plane Separation
Red plane( Optic Disk
detection)
Green plane( Microaneurysms and Blood vessels detection)
Blue plane
August 03,2010 7Confidential Information
Image
Processing and
Automation
algorithm
Flowchart
Stop
Detection of Microaneurysms using Bottom Hat transform and Morphological operators
Identification of Blood Vessels using Bottom Hat transform
Identification of Optic Disk using Hough Transform
Retinal Image acquisition
Retinal Image plane separation
Start
August 03,2010 8Confidential Information
Features of Optic Disk(OD)
Appears in color fundus images as a bright yellowish or white region
Its shape is more or less circular, interrupted by outgoing vessels
Its size varies between different patients and is approximately 50 pixels in 576 x 768 color photographs
August 03,2010 9Confidential Information
OD Detection Using Circular Hough Transform
Performed in red plane Edge detection Finds circles from an edge detected image Suitable radius is given as input (30 ≤ R ≤ 40) Falsely detected circular edges are eliminated by
selecting the largest circle
Original image OD marked imageAugust 03,2010 10Confidential Information
Image
Processing and
Automation
algorithm
Flowchart
Stop
Detection of Microaneurysms using Bottom Hat transform and Morphological operators
Identification of Blood Vessels using Bottom Hat transform
Identification of Optic Disk using Hough Transform
Retinal Image acquisition
Retinal Image plane separation
Start
August 03,2010 11Confidential Information
Blood Vessels(BV ) Detection
Acts as a landmark in grading disease severity
Prominent in green plane
Bottom Hat Transform Enhances details for a gray scale image It is given by the formula:
Bottom Hat image = (original image) – (closing image)
August 03,2010 12Confidential Information
Canny edge detector Uses two thresholds to
detect strong and weak edges
Noise resistant & highlights true weak edges
BV Detection Cont….
Canny Edge Detected Image
Thresholded Image
Thresholding It is used to highlight
the longer & connected vessels, thus eliminating smaller thread like structures
August 03,2010 14Confidential Information
Image
Processing and
Automation
algorithm
Flowchart
Stop
Detection of Microaneurysms using Bottom Hat transform and Morphological operators
Identification of Blood Vessels using Bottom Hat transform
Identification of Optic Disk using Hough Transform
Retinal Image acquisition
Retinal Image plane separation
Start
August 03,2010 16Confidential Information
Microaneurysms(MA) Detection
Appear more contrasted in green plane Candidate region possibly corresponding to MAs
selected
Canny Edge Detected Image BV Thresholded Image
Subtracted ImageAugust 03,2010 17Confidential Information
Suitable threshold window selected for exact detection of MA on subtracted image
Morphological operators used to find exact MAs on threshold image
MA Detection Cont…
Threshold Image
Detected MAs August 03,2010 18Confidential Information
Retinal Image acquisition from Ophthalmologist
Result comparison with that of Ophthalmologist
Image processing and automation algorithm in
MATLAB
Start
Stop
PROJECT
FLOWCHART
August 03,2010 22Confidential Information
MAs marked by Ophthalmologist MA s detected using MATLAB algorithm
Result comparison with that of Ophthalmologist
August 03,2010 23Confidential Information
MATLAB algorithm Performance Characteristics
Performance of the system is measured on the based on
Sensitivity and Accuracy Sensitivity can be computed by the following
equation: Sensitivity=TP/(TP + FN)
Accuracy can be computed by the following equation:
Accuracy=FP/(FP+FN) where,
TP is number of positives outcomes i.e. MAs accurately detected
FN is the abnormal sample classified as normal i.e. features which are MAs but not detected by the algorithm
FP is the number of negative outcomes i.e. features that are not MAs but detected as MAs
August 03,2010 24Confidential Information
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FN TP
Input Retinal Image
To
tal n
o. o
f M
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imag
e(T
P+
FN
)
Detection of MAs Manually Using MATALB algorithm
Sensitivity 78.05% 82.10%
Sensitivity Graph
Sensitivity Table
August 03,2010 25Confidential Information
Accuracy Graph
Accuracy of the MATLAB algorithm is found to be 80%
August 03,2010 26Confidential Information
Application Facilitates management of Diabetic Retinopathy
The system can potentially reduce the number of retinal images that the clinician needs to review by 60%
For preliminary detection of Diabetic Retinopathy by a general physician, especially in rural areas where Opthalmologists are not easily available
August 03,2010 27Confidential Information
Future Scope Along with MAs which are the primitive signs of DR,
long persisting features like Hemorrhages and newly formed Blood Vessels can be detected
Accuracy of the system can be further improved by considering broader and more enhanced specifications of the features like their inclination and connectivity to the neighboring blood vessels
Advanced types of DR like PDR, moderate NPDR and Diabetic Maculopathy can be diagnosed on similar lines
A high speed processor or better computer programming languages like visual C++, java etc can be used to improve the processing speedAugust 03,2010 28Confidential Information