Abstract— This paper presents an investigation into

different approaches for segmentation-driven retinal

image registration. This constitutes an intermediate step

towards detecting changes occurring in the topography

of blood vessels, which are caused by disease progression.

A temporal dataset of retinal images was collected from

small animals (i.e. mice). The perceived low quality of the

dataset employed favoured the implementation of a

simple registration approach that can cope with rotation,

translation and scaling, in the presence of major vascular

dissimilarities, distortions, noise, and blurring effects.

The proposed approach uses a single control point, i.e.

the centroid of the optic disc, and achieves accurate

registration by matching points in the pair of input

images using mean squared error calculation. A number

of alternative, more sophisticated methods have been

explored alongside the proposed one. While these other

methods could prove valuable and perform reasonably

well when applied on good quality images, they generally

fail when using the dataset at hand.

I. INTRODUCTION

egmentation is concerned with the partitioning of an

image into meaningful regions or objects, which share

different types of content, and is regarded as a necessary

step before further image analyse and information extraction.

In our application, blood vessels and the optic disc are the

objects of interest. Moreover, in retinal image processing,

segmentation can often be employed in order to guide

registration. Retinal image registration aims to achieve the spatial

alignment of two or more images of the same retina,

captured at different moments in time and from different

viewpoints. In general, image registration can be categorised

in four types of approaches: 1) elastic model-based; 2)

Fourier-domain based; 3) correlation-based; and 4) point

matching methods [1]. In retinal images, due to the type of

distortions occurring, the most common approach is by point

matching methods [3]. The distortions that retinal images

suffer from are caused by a number of different reasons [3]:

1) change in subject’s sitting position (large horizontal

translation); 2) change in chin cup position (smaller vertical

translation); 3) head tilting and ocular torsion (rotation); 4)

L.Andreou and A. Achim are with the Department of Electrical and

Electronic Engineering, University of Bristol, Merchant Venturers Building, Woodland Road, Bristol BS8 1UB, UK l.andreou06@bristol.ac.uk;

alin.achim@bristol.ac.uk

distance change between the eye and the camera (scaling); 5)

the three–dimensional nature of the retinal surface (spherical

distortion); and 6) inherent aberrations of the eye and camera

optical systems. When capturing retinal images from mice

rather than humans these effects are more pronounced and

consequently cause more distortions and image degradation.

The main goal of this study was to develop an image

analysis application using animal models that would then be

translated into clinical practice for application on patients

suffering from diabetes and uveitis. These are diseases that

can progress unpredictably and very rapidly to threaten

sight.

The following sections progressively introduce the

different approaches attempted in order to achieve the

aforementioned goal. First, vascular segmentation

algorithms using wavelets and active contours are presented

and then the details of a registration technique that uses optic

disc detection to drive a point matching procedure based on

Mean Squared Error (MSE) calculation is introduced.

II. THEORETICAL PRELIMINARIES

In this section we provide a brief theoretical background on

the main concepts on which our algorithms are based.

1) The ‘a trous’ Wavelet Transform: Is a fast, shift

invariant wavelet transform. The wavelet is defined as [5]:

1

( )2 2 2

x xxψ φ φ= −

(1)

where ( )xψ is the wavelet function and ( )xφ is the low-pass

scaling function. Thus, each pixel of the original signal can

be reproduced by the summation of all the wavelet scales

and the smoothed array, Jc :

0, , ,

1

J

k J k j k

j

c c w=

= + ∑ (2)

where j is the scale and k is the pixel position .

2) K-Means Clustering is a classification algorithm that

groups data into K clusters by minimising the sum of

distances between data and the cluster centroid squared (i.e.

Euclidean distance). The criterion needed to be iteratively

minimized is ([5], ch.9):

21

| | q Q i qI ∈ ∈

−∑∑ i q� � (3)

where I is the dataset, | . | indicates the instances of I, q is a

cluster, Q is the partition, i is a vector of the dataset I and q

is the cluster mean and equivalently the centroid:

Temporal Registration for Low-Quality Retinal Images of the

Murine Eye

Lenos Andreou and Alin Achim

S

32nd Annual International Conference of the IEEE EMBSBuenos Aires, Argentina, August 31 - September 4, 2010

978-1-4244-4124-2/10/$25.00 ©2010 IEEE 6272

1

| | i qq ∈

= ∑q i (4)

Each iteration q is updated until no further minimisation is

possible [7].

3) Multiscale Products (MSP) is simply the product of

the different scales in which an image is decomposed when

the ‘a trous’, is applied. The purpose of such an operation is

to enhance edge coefficients. Mathematically, this can be

represented as [10]:

1

( , ) ( , )J

J i

i

P x y W x y=

= ∏ (5)

where JP is the correlation image, J is the highest scale at

which the correlation is evaluated and ( , )iW x y is the wavelet

coefficient at scale i and location (x,y).

4) Active Contours (snakes) represent an energy

minimization process, where an initial contour is formed

which iteratively deforms in order to minimize an energy

functional. The energy functional is given by (ch.6.3, [7]):

1

int

0

( ( )) ( ( )) ( ( ))snake image con

s

E E s E s E s ds=

= + +∫ v v v (6)

where intE , imageE and conE are the different energy

contributions based on further calculations which provide

the parameters and the capability of controlling the

behaviour of the snake. Energy contributions originate from

functions that depend among other factors on bending,

elasticity, intensity, and edge.

Different snake variants have been developed with

different energy contributions, after M. Kass et al. [8]

proposed this approach. A particular snake algorithm that is

faster and more sensitive to edge and slope is the Gradient

Vector Flow (GVF) Snake [9]. It computes an edge map in

order to produce an external force field that replaces the

potential force field in the general form of parametric active

contour introduced by M. Kass et al.

III. ALGORITHMIC DEVELOPMENT

The current section describes the different new techniques

developed in the attempt to compute an integrated solution

for automatic segmentation-driven retinal image registration.

A. Vessels Segmentation

1) Combined A Trous/K-Means algorithm: After a

careful review of commonly employed segmentation

techniques retinal images [2][4], the A Trous (or Stationery

Wavelet Transform (SWT)) was selected as the basis for

developing our own algorithm. The other techniques

considered included thresholding, region growing, JSEG,

watershed and several edge detection techniques in the

spatial and frequency domain. The proposed algorithm is a

combination of K-Means Clustering and ‘A trous’ wavelet

transform as shown in the block diagram of Fig. 1.

We proceed by performing colour segmentation via K-

Means in L*a*b colour space both for the higher scales

wavelet coefficients and the original image.

Fig. 1: “A Trous/K-Means”

We then combine the colour segmentation with the edge

detection and denoising achieved by MSP, and hence

segment visible vessels better than all other techniques

considered.

The results were encouraging, but due to the low-quality

images in our dataset the vessels were fragmented and also

some small non-vascular regions were falsely segmented as

well.

2) GVF Snake: In order to address the above

fragmentation issue we applied an “area thresholding” first,

to remove the non-vascular regions, and then GVF Snake to

defragment the vessel tree as shown in Fig. 2.

Fig. 2: Defragmentation and cleaning process

We show example results of applying these techniques in the

Results section.

B. Optic disc detection and MSE-based Registration

1) Optic disc detection: Optic disc, sometimes also

referred to as optic nerve head, is generally the brightest

region of the fundus and there are many techniques for its

detection [2]. We were able to perform optic disc detection

by emplying a sequential process that enables the calculation

of its centroid. The optic disc detection procedure is shown

diagrammatically in Fig. 3.

Fig. 3: Optic disc localization

A description of each block in the diagram above is provided

in the following

‘A Trous’ Wavelet

TransformL*a*b Conversion

K-Means

Clustering

L*a*b Conversion

Image

ba

K-Means

Clustering

ba

Multiscale

Products

Thresholding

Segmented

Vessels

Colour

Segmentation

Edge

detection and

Denoising

6273

a) HSV represents the conversion of our RGB

images into HSV colourspace, which is more sensitive to

luminance than chrominance and more sensitive to high

contrast regions than to low contrast region being thus more

appropriate for optic disc localisation.

b) Thresholding is applied to the high

magnitude image pixels, i.e the largest value ‘bin’ of the

equalized histogram.

c) Dilation-Erosion represent morphological

processes to connect neighbouring segmented pixels of high

luminance in order to form regions of high luminance.

d) Area Thresholding is needed since the optic

disc is the largest region of connected high luminance pixels.

Thus, all regions smaller than the largest one are discarded.

e) Centroid Calculation is used in order to

calculate the centroid of the largest area.

2) MSE-based Registration:

We start the description of this part of our proposed

algorithm by first depicting again its block diagram below.

Fig. 4: Block diagram of MSE-based Registration

As before, we now provide a succinct description of each

block:

a) Size Compensation: Using the two

segmented optic discs from the detection process, calculate

the size difference between them. Thus compensate for any

size difference that can affect registration between the two

retinal images.

b) Segment Blocks: The image block extracted

for each image contains the optic disc as well as some of the

surrounding area. Consequently, by confining the two

images to be matched to some region around the optic disc,

the vascular spherical distortion effects that would have

misled the matching process are minimised. Also, the

regions contain less non-vessel background that causes

unwanted averaging in the MSE.

c) Rotate Image 2: Rotate ‘image 2’ block by

‘k’degrees, (‘k’<1o ). d) MSE: Compute MSE i.e. (7), between

‘image 1’ block and rotated ‘image 2’ block. Repeat step b)

and c) for some degree range (e.g. 0 360o o− ) keeping track

of lowest MSE and the degree at which it occurs (i.e.

‘matching degree’)

1 1

2

0 0

1( )

N M

ij ij

i j

MSE C RMN

− −

= =

= −∑ ∑ (7)

e) Register: Translate ‘image 1’ and ‘image 2’

in order for the centroid to coincide with their centre and

rotate ‘image 2’ by the ‘matching degree'.

IV. RESULTS

We performed extensive experiments in order to assess the

quality of our developed algorithms. Different qualitative

assessment measures were proposed in the literature to

assess image segmentation/registration results [11]. These

include accuracy/precision, robustness, algorithm

complexity, assumptions verification, and execution time.

For the purpose of this communication, we quantified our

results by visually assessing them in light of the

aforementioned measures, as well as by providing

execution times.

A. Evaluation of Retinal Vessels Segmentation Algorithm

(a) (b)

(c) (d)

Fig. 5: (a) Original retinal image. (b) Focused version.

(c)Segmentation using ‘A Trous/K-Means’ algorithm. (d)

Defragmentation and cleaning process performed on (c).

We first applied the technique presented in Sections III.A.1)

and III.A.2) to the images in our database, one typical result

being shown in Fig. 5. Fig. 5(d) shows that the vessels that

were visible enough to be segmented are almost perfectly

outlined with almost unnoticeable extra segmentation. This

was a consistent result, only the degree of fragmentation

varying across the images in our database.

Nevertheless, the whole process is relatively complex

compared with the other techniques mentioned in Section

III.A. The execution time was 60.12s on an Intel Centrino

Core 2 @ 2GHz, for a 500x500 retinal image.

Although relatively good from a segmentation point of view,

this method did not allow any reasonable registration result.

This was largely due to the fact that the major assumption

6274

made in implementing it was that the vessels must cross.

Any vessel-based point matching technique relies on finding

vascular crossing points in order to be used as anchor points

in a registration process, which due to the low

images was rarely the case.

B. Evaluation of Image Registration Algorithm

(a) (b)

(c) (d)

(e)

Fig. 6: (a) Image 1 (b) Image 2 (c) Image 1 block

2 block (e) Reference Image 1 (f) Registered

(a) (b)

(c)

Fig. 7: (a) Image 1 segmented optic disc. (b) Image 2

segmented optic disc. (c) Ref. Image 1. (d) Reg. Image 2.

was that the vessels must cross.

based point matching technique relies on finding

vascular crossing points in order to be used as anchor points

low-quality of our

Algorithm

(a) (b)

(c) (d)

(f)

Image 1 block (d) Image

istered Image 2.

(a) (b)

(d)

Fig. 7: (a) Image 1 segmented optic disc. (b) Image 2

segmented optic disc. (c) Ref. Image 1. (d) Reg. Image 2.

We first assessed the quality of the

of our algorithm both for the case of murine eye

and for images of human retina (available

Fig. 7). By observing Fig. 6 (c) and (d) it can be seen that

this has been accurately achieved. In terms of

registration, clearly both the mice images

images were registered with high accuracy

The execution time of our algorithm

search with a step of 10 was 16.42s on

2 @ 2GHz, for a 600x600 retinal image

V. CONCLUSIONS AND FUTURE WORK

We presented the results of a study aimed at developing

temporal registration of retinal images

preliminary segmentation of blood vessels or optic disc

The registration approach that we devised

with rotational, translational and size

relying on the vessels for point matching.

not only can be used for the typical good

images, but more importantly for cases

not possible using the segmented vessels.

optic disc detection on which the algorithm actually relies is

of theoretical importance in its own right since it constitute

the starting point in many other processing algorithms

Our current work focuses on des

algorithms for the temporally pre-registered retinal images

to enable automatic correlation of vessels topography

changes with specific disease progression. Results will be

presented in a future communication

ACKNOWLEDGMENT

The authors would like to thank Dr Lindsay Nicholson from

the School of Medical Sciences at the University of Bristol

for providing the retinal images used in this study.

REFERENCES

[1] L. G. Brown: “A survey of image registration techniques,”

Computing Surveys, vol. 24, no. 4, pp. 325–376, 1992.[2] N. Patton et al.: “Retinal image analysis: Concepts,applications and

potential,” Progress in Retinal and Eye Research, vol.25, pp. 99

[3] F. Laliberté, L. Gagnon and Y. Sheng: “Registration and Fusion of Retinal Images—An Evaluation Study,” IEEE Trans. Med. Imag., vol. 22,

pp. 661–673, May 2003.

[4] M. S. Mabrouk, N. H. Solouma and Y. M. Kadah: “Survey of Retinal Image Segmentation and Registration,” GVIP Journal, vol. 6, issue 2, 2006.

[5] J.-L. Starck, F. Murtagh: “Astronomical Image and Data Analysis,”

2nd ed. Springer–Verlag Berlin Helderberg, 2006[6] Lloyd, P.: “Least squares quantization in PCM,” Technical report, Bell

Laboratories,1957.

[7] M. S. Nixon and A. S. Aguado: “Feature Extraction and Image Processing,” 1st ed. Butterworth-Heinemann, 2002.

[8] M. Kass, A. Witkin, and D. Terzopoulos: “Snakes: Active contour

models.” Int. J. Computer Vision, 1(4):321–331, 1987.[9] C. Xu and J. L. Prince: “Gradient Vector Flow: A New External Force

for Snakes,” IEEE Proc. Conf. on Comp. Vis. Patt. Recog.(CVPR' 97).

[10] J.-C. Olivo-Marin:“Extraction of spots in biological images using multiscale products,”Pattern Recognition, Vol. 35, No. 9, pp.1989

[11] J.B.A. Maintz and M.A. Viergever: “A Survey of medical image registration,” Oxford Uni. Press, Med. Imag. Analysis

[12] Image Sciences Institute. 2001-2009: “

Vessel Extraction: DRIVE database.” [Online] (Updated 29 December 2007). Available at: http://www.isi.uu.nl/Research/Databases/

November 2009].

We first assessed the quality of the optic disc detection part

case of murine eye (i.e. Fig. 6)

(available online [12]) (i.e.

By observing Fig. 6 (c) and (d) it can be seen that

this has been accurately achieved. In terms of MSE-based

mice images and the human

were registered with high accuracy.

of our algorithm for a full 0

0 360o −

16.42s on an Intel Centrino Core

z, for a 600x600 retinal image.

AND FUTURE WORK

We presented the results of a study aimed at developing

temporal registration of retinal images based on a

preliminary segmentation of blood vessels or optic disc.

approach that we devised is able to cope

with rotational, translational and size differences without

relying on the vessels for point matching. Thus the method

the typical good-quality retinal

for cases when registration is

vessels. Furthermore, the

optic disc detection on which the algorithm actually relies is

of theoretical importance in its own right since it constitutes

processing algorithms.

Our current work focuses on designing change detection

registered retinal images,

to enable automatic correlation of vessels topography

changes with specific disease progression. Results will be

presented in a future communication.

CKNOWLEDGMENT

thors would like to thank Dr Lindsay Nicholson from

the School of Medical Sciences at the University of Bristol

for providing the retinal images used in this study.

EFERENCES

L. G. Brown: “A survey of image registration techniques,” ACM

376, 1992. N. Patton et al.: “Retinal image analysis: Concepts,applications and

potential,” Progress in Retinal and Eye Research, vol.25, pp. 99–127, 2006.

F. Laliberté, L. Gagnon and Y. Sheng: “Registration and Fusion of An Evaluation Study,” IEEE Trans. Med. Imag., vol. 22,

M. S. Mabrouk, N. H. Solouma and Y. M. Kadah: “Survey of Retinal Image Segmentation and Registration,” GVIP Journal, vol. 6, issue 2, 2006.

onomical Image and Data Analysis,”

2006. Lloyd, P.: “Least squares quantization in PCM,” Technical report, Bell

M. S. Nixon and A. S. Aguado: “Feature Extraction and Image Heinemann, 2002.

M. Kass, A. Witkin, and D. Terzopoulos: “Snakes: Active contour

331, 1987. Gradient Vector Flow: A New External Force

Vis. Patt. Recog.(CVPR' 97).

“Extraction of spots in biological images using multiscale products,”Pattern Recognition, Vol. 35, No. 9, pp.1989-96, 2002.

J.B.A. Maintz and M.A. Viergever: “A Survey of medical image Oxford Uni. Press, Med. Imag. Analysis Vol 2 pp. 1-36, 1998.

: “Digital Retinal Images for

[Online] (Updated 29 December http://www.isi.uu.nl/Research/Databases/ [Accessed 10

6275