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CHAPTER 2

LITERATURE REVIEW

2.1 INTRODUCTION

The existing methods for optic disk, exudates and blood vessel

extraction are surveyed and some of the methods are described below. Some

of the points are taken from them.

2.2 DETECTION OF OPTIC DISK

The optic disk (OD) is the exit point of retinal nerve fibers from the

eye, and the entrance and exit point for retinal blood vessels. It is a brighter

region than the rest of the ocular funds and their shape are approximately

round. The location of the OD is crucial in retinal image analysis, for

example, as a reference to measure distances and identifies anatomical parts

in retinal images (e.g. the fovea), for blood vessel tracking, and many others.

Precise localization of optic disk boundary is an important sub problem of

higher level problems in ophthalmic image processing. Specifically, in

proliferate diabetic retinopathy, fragile vessels are developed in the retina,

largely in the optic disk (OD) region, in response to circulation problems

created during earlier stages of the disease.

2.2.1 Clustering and Principal Component Analysis (PCA) method

The intensity of the optic disk is much higher than the surrounding

retinal background. A common method of optic disk localization is to find the

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largest cluster of pixels with the highest gray level. A simple clustering

method (Huiqui Li and Opas Chutatape, 2001) was applied on the intensity

image to find the candidate regions where the optic disk may appear and

Principal component analysis (PCA) was applied only on these candidate

regions to locate the optic disk. This method was applied with the presence of

large area of light lesions.

2.2.2 Principal component analysis (PCA) and active shape model

Principal component analysis (PCA) was applied to the candidate

regions at various scales to locate the optic disk. The minimum distance

between the original retinal image and its projection on to "disk spaces"

indicates the center of the optic disk. The shape of optic disk was obtained by

an active shape method in which affine transformation (Huiqi Li and Opas

Chutatape, 2003 and Daehee Kim et al 2009) was used to transform the shape

model from shape space to image space. The effects of vessels present inside

and around the optic disk were not eliminated, but incorporated in the

processing. This algorithm took the advantage of the top-down strategy that

could achieve more robust results especially with the presence of large areas of

light lesions and when the edge of the optic disk was partly occluded by vessels.

2.2.3 Contour detection using ellipse fitting and wavelet transform

The contours were usually represented by image edges and image

features consisting of points where the image intensity function varies

sharply. It was crucial for the precise identification of the macula to enable

successful grading of macular pathology such as diabetic maculopathy.

However, the extreme variation of intensity features within the optic disk and

intensity variations close to the optic disk boundary presents a major obstacle

in automated optic disk detection. The presence of blood vessels, crescents

and peripapillary chorioretinal atrophy seen in myopic patients also increase

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the complexity of detection. A novel algorithm was used to detect the optic

disk based on wavelet processing and ellipse fitting. Daubechies wavelet

transform (Pallawala et al 2004) was used to approximate the optic disk

region. Next, an abstract representation of the optic disc was obtained using

an intensity-based template. This yielded robust results in cases where the

optic disc intensity was highly non-homogenous. Ellipse fitting algorithm was

then utilized to detect the optic disc contour from this abstract representation.

Additional wavelet processing was performed on the more complex cases to

improve the contour detection rate. Giri Babu Kande et al (2008) and Mendels

et al (1999) used active contour for the detection of optic disk. A snake was

used by Kass et al (1988) as an energy minimizing spline guided by external

constraint forces to pull it towards features such as lines and edges.

2.2.4 Sobel edge detection and least square regression

The Optic disk can be seen as a pale, well defined round or slight

vertical oval disk. The detection was performed in the red component in three

steps: candidate area identification, sobel edge detection and estimation step.

The candidate area was identified and a clustering algorithm (Huiqui Li and

Chutatape, 2000) was applied to assemble nearby pixels into clusters. The

gravity center of the largest cluster was defined as the center of the candidate

area. Sobel edge detector was applied to the candidate area to get the contour

of the optic disk. Performance wise it was not satisfied because of noises.

Hence LSR (Least Square Regression) was applied to get the estimated circle,

based on Sobel edge detector. Another method used texture descriptors

(Lupascu et al 2008) and a regression based method in order to determine the

best circle that fits the optic disk. The best circle was chosen from a set of

circles determined by the method.

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2.2.5 Lab color morphology

The location of the optic disk (OD) was of critical importance in

retinal image analysis by both the automatic initialization of the snake

(Alireza Osareh et al 2002) and the application of morphology in color space.

Previously optic disk (OD) was focused on locating its center (Sinthanayothin

et al 1999). The interference of blood vessels was removed by dilation and

erosion restores the boundaries. Standard gray level morphology was

performed after a transformation of the RGB color image in to the gray level

image. By fitting the snake onto the optic disk, the performance could be

measured for various morphological methodologies. The initial contour for

snake must be close to the disk boundary otherwise it could converge to the

wrong place. The accuracy obtained was 90%. In color morphology

(Hanburry and Serra, 2001), each pixel must be considered as a vector of

color components. Definitions of maximum and minimum operations on

ordered vectors were necessary to perform basic operations.

2.2.6 Simulated annealing optimization

The method was based on the preliminary detection of the main

retinal vessels by means of a vessel tracking procedure. All retinal vessels

originate from the optic disk and then follow a parabolic course towards

retinal edges. A geometrical parametric model (Ruggeri et al 2003) was

proposed to describe the direction of these vessels and two of the model

parameters are just the coordinates of the optic disk center. Using samples of

vessels’ directions (extracted from fundus images by the tracking procedure)

as experimental data, model parameters were identified by means of a

simulated annealing an optimization technique. These estimated values

provided the coordinates of the center of the optic disk.

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2.2.7 Circular Hough transform and local variance

The green band of the images is processed since that these images

have the greatest contrast between the optic disk and the retinal tissue. Firstly,

the blood vessels in the image were suppressed by morphological methods

(closing). Then 24 radial vectors were defined using the approximate centre

of the optic disk as the origin. The image was resampled along these vectors

to form a representation that was subsequently processed.

First, the image was firstly enhanced using the Sobel operator, and

then threshold was applied using the local mean and variance to compute the

threshold value. The remaining points were inputs to a circular Hough Transform

(Abdel Ghafara et al 2004) and the largest circle was the optic disk. The success

rate was only 65%.This method separates the normal and abnormal images due

to glaucoma. The optic disk appearance was uniform in normal images but

progressively less as the disease progresses. Thitiporn Chanwimaluang and

Guoliang Fan (2003) suggested two steps. The approximate center of the optic

disk was located using the maximum of the local variance and the boundary was

delineated by fitting the active snake. The local variance could indicate the

intensity variation of the optic disk which was related to blood vessel and small

structures inside the optic disk. For the large variance, more snake points should

be used to overcome the disturbing gradient from blood vessel and/or intensity

variations. High anatomical constraints were not involved.

2.2.8 Histogram Equalisation and Dynamic Contour

The retinal image was enhanced using histogram equalization.

Preprocessing was achieved by applying a pyramid edge detector to the

contrast enhanced image. Pixels in a layer of the pyramid data structure were

computed as the average of groups of four pixels and this averaging prevents

future examination of the spurious edge points arising from the noise. Edge

strength was used to fit the snake to the disc boundary. The cholesky

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algorithm (Morris, 1999) was used. But here the snake had failed to locate the

boundary in the upper right quadrant. The gradient vector flow (GVF) based

algorithms had been used successfully to segment a variety of medical

imagery. However, due to the compromise of internal and external energy

forces within the resulting partial differential equations, these methods could

lead to less accurate segmentation results in certain cases. The use of a new

mean shift-based GVF segmentation algorithm (Huiyu Zhou et al 2010)

derived the internal/external energies towards the correct direction. This

method incorporates a mean shift operation within the standard GVF cost

function to arrive at a more accurate segmentation.

2.2.9 Texture Based Method

At random, 23 images were chosen for training and the other 23 for

testing. In the training step, 3 blocks containing 1% were collected from each

image, which accounted for 69 blocks and used as the training set. The

weighted function was defined by linearly combining three regression lines

obtained by fitting three pairs of statistic parametric (Chu Yang-Mao et al

2005) relations which were used to precisely detect the optic disk regions and

this could effectively eliminate the incorrect optic disk detection caused by

extending bright areas around the optic disk. Optic disk (OD) localization

could also be achieved by iterative threshold method to identify initial set of

candidate regions followed by connected component analysis (Siddalingaswamy

and Prabhu, 2007) to locate the actual optic disk.

2.2.10 Pyramidal Approach

The optic disk tracking was done by pyramidal decomposition

(Gagnon et al 2001), and by Optic Disk contour search technique based on the

Hausdorff distance. The global Optic Disk region was found by means of a

multi-scale analysis (pyramidal approach) using a simple Haar-based wavelet

transforms. The brightest pixel that appears in a coarse resolution image

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(at an appropriate resolution level depending on the initial image resolution

and the Optic Disk average dimension) was assumed to be part of the optic

disk. This global optic disk (OD) localization serves as the starting point for a

more accurate optic disk (OD) localization obtained from a template-based

matching that uses the Hausdorff distance measure on a binary image of the

most intense Canny edges. An average error of 7% on optic disk (OD) center

positioning was reached with no false detection. In addition, a confidence

level was associated to the final detection that indicates the "level of

difficulty" the detector had to identify the optic disk (OD) position and shape.

Genetic based detection (Abraham Chandy and Vijaya Kumari, 2006) was

involved in the preprocessing stage having pixels with the highest 2% gray

levels. Simple clustering mechanism and optic disk diameter were considered

to determine the candidate region and the optic disk center. Genetic algorithm

explores the combinatory space of possible contours (solutions) by means of

crossover and mutation, followed by the evaluation of fitness and the

selection of a new set of contours. The cumulative local gradient was used as

a fitness function to find the fittest contour.

A method (Foracchia et al 2004) to identify the position of the optic disc

(OD) in retinal fundus images was based on the preliminary detection of the

main retinal vessels. All retinal vessels originate from the OD and their path

follows a similar directional pattern (parabolic course) in all the images. To

describe the general direction of retinal vessels at any given position in the

image, a geometrical parametric model was proposed, where two of the model

parameters were the coordinates of the OD center. Using as experimental data

samples of vessel centerline points and corresponding vessel directions,

provided by any vessel identification procedure, model parameters were

identified by means of a simulated annealing optimization technique. These

estimated values provide the coordinates of the center of optic disk.

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2.2.11 Topological Active Nets (TAN)

Gagnon et al (2001) extracted the optic disk using Hausdorff based

template matching and pyramidal decomposition. It was neither sufficiently

sensitive nor specific enough for clinical application. An average error of 7%

on Optic Disk center positioning was reached with no false detection.

In addition, a confidence level was associated to the final detection

that indicates the "level of difficulty" and the detector had to identify the

Optic Disk position and shape. Chr´astek et al (2002) used an automated

method for the optic disk segmentation which consists of 4 steps: localization

of the optic disk, nonlinear filtering, canny edge detector and Hough

transform. The nonlinear filtering was used as a method for noise reduction,

which at the same time preserve the edges. Since the optic disk is a circular

structure, the authors used the Hough transform in order to have a method of

circle detection.

The transform gave them the center and radius of a circle

approximating the border of the optic disk. A new approach to the optic disk

segmentation process was proposed by Novo et al (2008) in digital retinal

images by means of Topological Active Nets (TAN). This was a deformable

model used for image segmentation that integrates features of region-based

and edge-based segmentation techniques, being able to fit the edges of the

objects and model their inner topology. The optimization of the Active Nets

was performed by a genetic algorithm, with adapted or new ad hoc genetic

operators to the problem. The active nets incorporated new energy terms for

the optic disk segmentations, without the need of any pre-processing of the

images.

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2.3 DETECTION OF EXUDATES

2.3.1 Support Vector Machines and Neural Networks

Alireza Osareh et al (2001) used Gaussian–smoothed histogram

analysis and Fuzzy C means clustering to segment candidate exudates region

and Support Vector Machines (SVM) and neural network to classify the

exudates and non exudates. Support vector machine uses Structural Risk

Minimization approach. For separate classification, a kernel function was

used where a separating hyper plane (w, b) with w as the weight and b as the

bias was used which maximizes the margin or distance from the closest

points. The optimum separating hyper plane based on the kernel is

* + * +n

i i i

i 1

F x Sign y k x, x b?

à Ô? c -Ä ÕÅ Ö (2.1)

Where n is the number of training examples, yi the label value of i,

k is the kernel and gi the coefficient to maximize the Lagrangian

representation.

This can achieve a tradeoff between false positives and false

negatives using asymmetric soft margin. They converge to solution regardless

of initial condition and remove the danger of over fitting. A computer-based

approach (Jagadish Nayak et al 2008) for the detection of diabetic retinopathy

stage using fundus images includes morphological processing techniques and

texture analysis methods to detect the features such as area of hard exudates,

area of the blood vessels and the contrast. Maria et al (2009) used neural

network approach for the classification of exudates.

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2.3.2 Bayesian Statistical Classifier

Wang et al (2000) applied Bayesian statistical classifier based on

color features to differentiate the yellowish lesions. It combines brightness

adjustment procedure with statistical classification method and local window

based verification strategy. Objects in an image could be described in terms of

features such as color, size, shape, texture and other complex characteristics.

Different objects were classified into corresponding clusters by the rules. This

detects the lesions correctly but misclassifies the optic disk due to the

similarity in color. The presence of lesions could be preliminarily detected

using MDD (Maximal Dependence Decomposition) and brightness

adjustment procedure solved the non uniform illumination problem. The

accuracy was 70% in classifying the normal images. They did not measure

accurately to distinguish exudates among other lesions. The exudates

identified as exudates in gray level images based on a recursive region

growing technique. An adaptive threshold algorithm was developed

(Leistritz, 1994) to detect and to measure the exudates in gray value images of

patients with diabetic retinopathy. Ege et al (2000) suggested Mahalanobis

and Kohenen Neural Network approach along with Bayesian classifier for the

exudates detection.

Gardner et al (1996) used a Neural Network to identify the exudate

lesions in gray level images. A neural network was trained to recognize

features of diabetic fundus images and tested for sensitivity and specificity.

The network can be used to identify the presence of vessels, exudates and

hemorrhages with high predictive value. Back propagation neural network

was employed to analyse images using grids. The network was then tested

from the previously seen training data and the unseen test data. Neural

network determination of retinopathy for a whole image was based on the

statistical threshold and some squares in the grid may be missed. By altering

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the threshold, it is possible to change the networks detection rate to increase

the sensitivity and this leads to a lower specificity. The network training took

2 to 3 weeks, whereas this requires about an hour. We could also increase our

sensitivity and specificity results by changing the threshold on the network

output. This approach was very weak since not all of the exudates region may

appear within a square patch.

2.3.3 Fuzzy C-means clustering and Morphological Methods

Akara Sopharak et al (2007 and 2009) transformed Red, Green and

Blue (RGB) space to Hue, Saturation and Intensity (HSI) space. A median

filtering operation was applied to reduce noise before a Contrast-Limited

Adaptive Histogram Equalization (CLAHE) is applied for contrast

enhancement. CLAHE operates on small regions in the image. The contrast of

each small region was enhanced with histogram equalization. Fuzzy C-means

clustering (FCM) was used for segmentation and the fine segmentation using

morphological reconstruction (Alireza Osareh et al 2003).

The algorithm still had some false detection because some pixels

with similar color to the exudates belong to the optic disk and the edge of

blood vessel. Candidate exudates can be detected using a multi-scale

morphological process (Alan D Fleming et al 2007). Based on local

properties, the likelihood of a candidate being a member of classes exudates,

drusen or the background were determined. This leads to a likelihood of the

image containing exudates which could be thresholded to create a binary

decision.

Ahmed Wasif Reza and C. Eswaran (2008) used green component

of the image and preprocessing steps such as average filtering, contrast

adjustment, and thresholding. The other processing techniques used were

morphological opening, extended maxima operator, minima imposition, and

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watershed transformation. The algorithm was evaluated using the test images

with fixed and variable thresholds. Sato et al (1998) employed filter for the

segmentation. Russell et al (1993) employed thresholding for the detection of

exudates.

2.3.4 Region growing and Edge detection

Principal component analysis (Huiqi Li and Opas Chutatape, 2003)

was employed to locate the optic disk. A modified active shape model was

proposed in the shape detection of optic disk. A fundus coordinate system was

established to provide a better description of the features in the retinal images.

Exudates were detected by the combined region growing and edge detection

approaches. LUV was selected as the suitable color space for the exudates

detection. The fundus image was divided in to sixty four sub images.

Detection was done in each sub image. The color difference of the object

could be defined as

D(I,j)=Á (L(i,j)-Lr)2 + (U(i,j)-Ur)

2 (2.2)

L (i, j) and U (i,j) are the colors of pixel(i, j) in the component

L and U respectively. Lr and Ur are the reference colors of the object. The

reference color was determined as the gravity centre of the object. Mean

squared Wiener filter removed the noise. A combined method of region

growing and edge detection which includes seed selection, edge detection and

growing criteria was employed to detect the exudates. Edges in the sub

images were detected by the canny edge detector. Specificity obtained was

low.

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2.3.5 Recursive Region Growing Segmentation

An automated screening system (Sinthanayothin et al 2002) to

analyse the features of non-proliferative diabetic retinopathy (NPDR) includes

recursive region growing segmentation algorithms combined with the use of a

new technique, termed a `Moat Operator’. These were used to automatically

detect features of NPDR. These features included haemorrhages and

microaneurysms (HMA), which were treated as one group, and hard exudates

as another group. This presents encouraging results in automatic identification

of important features of NPDR. Cree et al (2004) employed Morlet wave

transformation to spot diabetic retinopathy.

2.3.6 K nearest Neighbour and Neural Network

Screening to detect retinopathy disease can lead to successful

treatments in preventing visual loss. Intraretinal fatty (hard) exudates are a

visible sign of diabetic retinopathy and also a marker for the presence of

co-existent retinal oedema. The retinal images were automatically analysed in

terms of pixel resolution and image-based diagnostic accuracies and an

assessment of the level of retinopathy was derived. To estimate the exudates

and non-exudates probability density distributions, K nearest neighbour,

Gaussian quadratic and Gaussian mixture model classifiers were utilized. This

includes colour image segmentation and region-level classification based on

neural network and support vector machine classifier models. A sliding

windowing technique (Gerald Schaefer and Edmond Leung, 2007) was also

used to extract parts of the image which were then passed on to the neural

network to classify whether that area was a part of exudates region or not.

Principal component analysis (PCA) and histogram specifications were used

to reduce training time and complexity of the network and to improve the

classification rate.

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2.3.7 Fuzzy C-means clustering and Neural Network

In this approach, the detection of exudates regions were done using

image color normalisation, enhancing the contrast between the objects and

background, segmenting the color retinal image (Alireza Osareh et al 2001).

The exudates and non exudates patches were determined by a neural network.

The color normalisation was done using histogram specification. Local

contrast enhancement improves both the contrasting attributes of lesions and

the overall color saturation in an image. Along with this Fuzzy C-means

clustering (FCM), the salient regions were highlighted and relevant features

were extracted and finally the exudates were classified using perceptron

neural network. This was performed on the intensity channel of the image

after it was converted to the HIS color space. The optic disk was segmented

as fragmented of exudates regions due to the similarity of its color to

yellowish exudates regions. The false detected optic disk regions were

ignored. The segmented objects were classified as exudates/non exudates

regions by a neural network.

2.3.8 Intelligent System

In the intelligent system, image Contrast was enhanced by means of

a neurofuzzy subsystem where fuzzy rules were implemented using Hopfield

type neural network (Leonarda Carnimeo and Antonio Giaquinto, 2006). This

was developed to behave as a fuzzy system to highlight pale regions in the

fundus images of patients. Multilayer perceptron neural network was trained

for evaluating the optimal global threshold which could minimize pixel

classification errors. The performance was compared with the algorithms like

Gradient descent backpropagation, gradient descent momentum with adaptive

learning rate, conjugate gradient backpropagation with Fletcher Reeves updates

and Levenberg-Marquardt backpropagation. The globally optimal segmentation

generates binary images which contain only significant information about

suspect damaged retinal areas. Kirsch et al (1971) used a number of

classification algorithms for pattern recognition in biomedical images.

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2.3.9 Adaptive Multiscale Morphological Processing

The diabetic retinopathy related spot lesions of various sizes and

shapes were accomplished by an adaptive multiscale morphological

processing technique (Xin Zhang and Guoliang Fan, 2006). Local minima

were the reference points to determine the proper scale for dark lesions and

two-sided edge model was used to define the lesion area. Segmentation of

dark and bright lesions was obtained by using entropy based thresholding

techniques. In this approach, the limitation was that lesion validation might

remove some dark lesions that were connected with blood vessels.

2.3.10 Machine Learning Based Automated System

A machine learning based automated system (Meindert Niemeijer

et al 2007) was capable of detecting exudates and cottonwool spots and

differentiates them using statistical classifier. Here each pixel was classified

and lesion probability map indicates the probability that a pixel was a part of a

bright lesion. Pixels with high probability were grouped into clusters. The

classifier differentiates the different types of pixels or clusters based on the so

called features or numerical characteristics such as pixel color or cluster area.

This gave outputs whether bright lesions were present or not and the type of

class of each lesion. In this technique, the bright lesions may be missed by

human experts and the automated system because of the limitations of the

digital two field non stereo photography. Secondly there could be a constraint

of the quality of annotations of the training image. A large number of training

pixels may improve the system performance. Retinal thickening, if present,

will be missed by the algorithm. The testing can be done only on a small

number of patients.

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2.3.11 Contextual clustering and Fuzzy Art Neural Network

There was an extensive dissimilarity in the color from different

patients owing to intrinsic attribute of lesions, decreasing color dispersion at

the lesion periphery and lighting disparity which results in color of lesion of

some images lighter than the background color. Jayakumari and Santhanam

(2007) applied Histogram equalization independently for each RGB channel

followed by enhancement. The contrast enhancement algorithm not only

enhances the brightness of lesions but also augments the brightness of the

surrounding pixels so that these may be recognized as class lesion. The

segmented images were discriminated to locate the hard exudates using

features such as convex area, solidity and orientation. The exudates were

detected with 93.4% sensitivity and 80% specificity. This method cannot

detect soft exudates which is its limitation.

2.4 DETECTION OF BLOOD VESSEL

Pattern recognition techniques deal with the automatic detection or

classification of objects or features. Humans are very well adapted to carry

out pattern recognition tasks. Some of the pattern recognition techniques are

the adaption of humans’ pattern recognition ability to the computer systems.

In the vessel extraction domain, pattern recognition techniques are concerned

with the detection of vessel structures and the vessel features automatically.

Various techniques are as follows:

(1) Multi-scale approach

(2) Skeleton-based (centerline detection) approach

(3) Region growing approach

(4) Ridge-based approach

(5) Differential Geometry-based approach

(6) Matching filters approaches and

(7) Mathematical morphology schemes

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2.4.1 Multi-scale Approach

Chwialkowski et al (1996) accomplished segmentation of blood

vessels using multi resolution analysis based on wavelet transform. The aim

was at automated qualitative analysis of arterial flow using velocity-sensitive,

phase contrast MR images. Multi-scale approaches perform segmentation task

on different image resolutions. The main advantage of this technique was the

increased processing speed. Major structures which were the large vessels in

our application domain were extracted at low resolution images while fine

structures were extracted at high resolution. Another advantage was the

increased robustness. After segmenting the strong structures at the low

resolution, weak structures such as branches in the neighborhood of already

segmented structures can be segmented at higher resolution.

2.4.2 Skeleton-Based (Centerline Detection) Approach

Skeleton-based methods extract blood vessel centerlines. The

vessel tree was then created by connecting these centerlines. Niki et al (1993)

described their 3D blood vessel reconstruction and analysis method. Vessel

reconstruction was achieved on short scan cone-beam filtered back

propagation reconstruction algorithm based on Gulberg and Zeng’s work.

A 3D thresholding and 3D object connectivity procedure were applied to the

resulting reconstructed images for the visualization and analysis process. A

3D graph description of blood vessels was used to represent the vessel

anatomical structure.

2.4.3 Ridge-Based Approach

Ridge-based methods treat grayscale images as a height map in

which intensity ridges are the approximation to the central skeleton of the

tubular objects. Thus a 2D image can be viewed as a 3D surface, image

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intensity forming the third dimension. Guo and Richardson (1998) proposed a

ridge extraction method that treats digitized angiograms as height maps and

the centerlines of the vessels as ridges in the map. The image was first

balanced by a median filter and then smoothed by a non-linear diffusion

method, anisotropic smoothing as the preprocessing step. Then a Region of

Interest (ROI) was selected by adaptive thresholding method. This process

cuts the cost of the ridge extraction process as well as reducing the false

ridges introduced by the image noise. Next the ridge detection process was

applied to extract the vessel centerlines. Finally the candidate vessel

centerlines were connected together from the extracted ridges using a curve

relaxation process.

2.4.4 Region Growing Approach

Region growing technique segments image pixels into regions that

are belonging to an object. Segmentation was performed based on some

predefined criteria. The simplest form of the segmentation can be achieved

through thresholding and component labeling. Segmentation process then

uses region boundary information to extract the regions. The main

disadvantage of region growing approach is that it often requires a seed point

as the starting point of the segmentation process. This requires user

interaction. Due to the variations in image intensities and noise, region

growing can result in holes and over segmentation. Thus it sometimes

requires post-processing of the segmentation result.

Schmitt et al (2002) combined thresholding with region growing

technique to segment vessel tree in 3D in their work of determination of the

contrast agent propagation in 3D rotational XRA image volumes.

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2.4.5 Differential Geometry-Based Approach

Differential geometry-based method treats images as hyper surfaces

and extracts features of the images using the curvature and the crest lines of

the surface. The crest points of the hyper-surface correspond to the center

lines of the vessel structure. The 2D and 3D images were treated in a similar

way. They only differ in their mathematical formulation. The principal

curvatures correspond to the Eigen values of the Weingarten matrix and the

principal directions were the eigenvectors. Crest points, which were the

intrinsic properties of the surfaces, were the local maxima of the maximum

curvature on the hyper surface.

Krissian et al (1998) described their Directional Anisotropic

Diffusion method which was derived from Gaussian convolution to reduce the

noise in the image. The algorithm was applied to a set of phantom images

containing torus with different radii and a set of real images of vessels.

2.4.6 Matching Filters Approach

Matching filters approach convolves the image with multiple

matched filters for the extraction of objects of interest and the vessel contours

in the vessel extraction domain. In extracting vessel contours, designing

different filters to detect the vessels with different orientation and size plays a

crucial role. The size of the convolution kernel affects the computational load

of the filtering process. Matching filters are usually followed with some other

image processing operations like thresholding and then connected component

analysis to get the final vessel contours. In the detection of vessel centerlines,

connected component analysis is preceded by thinning process.

Poli and Valli (1997) developed an algorithm to enhance and detect

vessels in real time. The algorithm was based on a set of multiple oriented

linear filters obtained as linear combination of properly shifted Gaussian

kernels. These filters were sensitive to vessels of different orientation and

thickness.

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2.4.7 Mathematical Morphology Schemes

Morphology relates to the study of object forms or shapes. It

facilitates the segmentation and search for object of interest by filling holes

and eliminating unwanted segments.

Figueiredo and Leitao (1995) described their non-smoothing

approach in estimating vessel contours in angiograms. All local maxima for

each vessel cross section of the morphological edge detector were considered

as candidates to edge points. The dynamic programming was used to find the

minimum cost path among the candidates by selecting a pair for each cross

section. Donizelli et al (1998) combined mathematical morphology and region

growing algorithms to segment large vessels from digital subtracted

angiography images. The author implemented three other classical and

morphological algorithms, multiphase analysis process, region splitting

approach, morphological-thresholding and compared them with this method.

Due to the binary region growing algorithm employed, this work can also be

classified as a region growing approach. A bilinear top hat transformation and

matched filters were used for the initial segmentation of the images

(Spencer et al 1996).

2.4.8 Model-Based Approach

In model-based approach, explicit vessel models are applied to

extract the vasculature. Model-based approaches are divided into four

categories:

(1) Deformable models

(2) Parametric models

(3) Template matching and

(4) Generalized cylinders.

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2.4.9 Deformable Models

The deformable models are categorized in to parametric deformable

models and geometric deformable models.

2.4.9.1 Parametric Deformable Models - Active Contours (Snakes)

Deformable models are model-based techniques employed for

finding object contours using parametric curves that deform under the

influence of internal and external forces.

Molina et al (1998) used 3D snakes to reconstruct 3D catheter paths

from biplane angiograms. In the image preprocessing step, geometric

distortions in both images introduced by the X-ray projections of the vessels

were corrected. The 3D snake used in this method was represented by

B-spines and initialized interactively. Using a snake, facilitates the merging

information from both projections simultaneously during the energy

minimization process.

Kozerke et al (1999) used a modified definition of the active

contour models in their technique to automatically segment vessels in phase

contrast flow measurements. The method requires the user to select the vessel

of interest in an arbitrary image frame by a mouse click. First, each phase

frame was convolved with a Gaussian mask to reduce noise. Next, isolated

pixels were removed and the holes were filled using connectivity information.

Finally, the first phase image in time with an area of half of the

maximum found overall was selected. In the sequential processing of the

remaining frames, segmented contours of the previous frame which was

temporarily neighboring the current frame was used as a model for the

approximation of the contour in the current frame in case of missing or

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distorted edge features. The method uses phase image, in addition to the

magnitude image, to handle image distortions.

Geiger et al (1995) proposed a method for detecting, tracking, and

matching deformable contours. The method was based on the dynamic

programming (DP) but it was non-iterative and guaranteed to find the global

minimum. Detection algorithm creates a list of uncertainty points for each

point selected by the user. Next the dynamic programming algorithm was

applied to find the optimal contour passing through these lists. Deformable

model was obtained, after considering all possible contours and deformations.

Matching was achieved through a strategy developed which uses a cost

function and some constraints and based on dynamic programming method.

Toledo et al (2000) combined a probabilistic principal component

analysis (PPCA) technique with a statistical snake technique for tracking non-

rigid elongated structures. Probabilistic principal component analysis

technique was used to construct statistical image feature descriptions while

snakes were used for global segmentation and tracking the objects. The

statistical snake learns and tracks image features using statistical learning

techniques.

2.4.9.2 Geometric Deformable Models and Front Propagation Methods

Quek and Kirbas (2001) developed an approach for the extraction

of vasculature from angiography images by using a wave propagation and

trace back mechanism in 2D. Using a dual-sigmoid filter, the system labels

each pixel in an angiogram with the likelihood that it is within a vessel.

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2.4.10 Parametric Model

Parametric model approach defines objects of interest

parametrically. In tubular object segmentation, objects are described as a set

of overlapping ellipsoids. Pellot et al (1994) employed deformable elliptic

model to approximate irregular vessels and bifurcations.

Krissian et al (1998) developed a multi scale model to extract and

reconstruct 3D vessels from medical images. This method used a new

response function which measures the contours of the vessels around the

centerlines.

Bors and Pitas (1998) used a pattern classification-based approach

for 3D object segmentation and modeling in volumetric images. The objects

were considered as a stack of overlapping ellipsoids whose parameters were

found using the normalized first and second order moments. The method

employed a radial Basis Function (RBF) network classifier in modeling the

3D structure and gray level statistics. The learning of the Radial Basis

Function (RBF) network was based on the Trimmed Mean algorithm.

2.4.11 Template Matching

Template matching tries to recognize a structure model (template)

in an image. The method uses the template as a context, which is a prior

model. Thus it is a contextual method and a top-down approach. In arterial

extraction applications, the arterial tree template is usually represented in the

form of a series of nodes connected in segments. This template is then

deformed to fit the structures in the scene optimally. Stochastic deformation

process described by a Hidden Markov Model (HMM) is a method to achieve

template deformation. Dynamic programming is an effective method

employed in recognition process.

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Petrocelli et al (1993) presented their method, based on a Gauss-

Markov model, to recognize three dimensional vessel structures from bi-plane

angiogram images. A unique feature of the system comes from its ability to

extract structures from unprocessed standard digital angiograms without any

preprocessing. The system, named Deformable Template Matcher (DTM),

utilizes a prior knowledge of the arterial tree encoded as mathematical

templates. An arterial tree template was represented in the form of a series of

nodes connected in segments. This template was created by a cardiologist

using an interactive mouse- driven program. This template was then deformed

with a stochastic deformation process described by a Hidden Markov Model

(HMM) to fit the unknown scene in the image using local image information.

The template was considered to fit the scene when the best of the state

transition was found. Since the system was working in 3D, the deformation

process was performed in space and back projected on to two planes used. It

requires a good model of the global structure and computational complexity

to extract entire vascular structure.

2.4.12 Generalized Cylinders Model

Generalized cylinders (GC) are used to represent cylindrical

objects. Technically, Generalized cylinders are parametric models.

Binford et al (1971) introduced the use of Generalized cylinders

(GC) in vision applications. Generalized cylinders (GC) consist of a space

curve, or axis and a cross-section function defined on that axis according to

the definition of Agin et al (1976). Cross-section function was usually an

ellipse. In tube-like object extraction, tubular objects were defined by a cross

sectional element that was swept along the axis of the tube (spine) using some

sweep rules. The spine was represented by a splice and the cross-section

function was represented as ellipse. Another method to represent cylinders

was to use Frenet-Serret formulation as the basis of GC. However, Frenet-

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Serret formulation model and tube model described earlier suffer from some

serious drawbacks. One drawback was that the cross-section function was not

kept orthogonal to the spine. Discontinuities and non-intuitive twisting

behavior were the other drawbacks. To alleviate these problems, researchers

developed some other Generalized cylinders (GC) models. One of these

models is the Extruded Generalized Cylinders (EGC) developed by

O’Donnell et al (1997).

Fessler and Macovski (1991) developed an object-based method for

reconstructing arterial trees from a few projection images. The method used

an elliptical model of generalized cylinders to approximate arterial cross-

sections. By incorporating a prior knowledge of the structure of arteries, the

problem of reconstruction turns into an object estimation problem. The

method employed a nonparametric optimality criterion that attempts to

capture arterial smoothness incorporated in the system as a prior knowledge.

2.4.13 Tracking-Based Approach

Tracking-based approaches apply local operators on a focus known

to be a vessel and track it. On the other hand, pattern recognition approaches

apply local operators to the whole image. Vessel tracking approaches, starting

from an initial point, detect vessel centerlines or boundaries by analyzing the

pixels, orthogonal to the tracking direction. Different methods are employed

in determining vessel contours or centerlines. Edge detection operation

followed by sequential tracing by incorporating connectivity information is a

straightforward approach.

Aylward et al (1996) utilized intensity ridges to approximate the

medial axes of tubular objects such as vessels. Some applications achieve

sequential contour tracing by incorporating the features, such as the vessel

central point, the searching direction, and the search range, detected from

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previous step into the next step. Fuzzy clustering was another approach to

identify vessel segments. It used linguistic descriptions like “vessel” and “non

vessel” to track vessels in retinal angiogram images.

Haris et al (1997) combined a recursive sequential tracking

algorithm and morphological tools of homology modification and watersheds

to automatically extract coronary arteries from angiogram images. The initial

segmentation of skeleton and borders of the coronary artery tree was achieved

through an artery tracking method based on circular template analysis.

Authors of the paper admit that the system had problem in extracting a

complete coronary tree.

2.4.14 Artificial Intelligence-Based Approach

Artificial Intelligence-based approach utilizes the knowledge-based

to guide the segmentation process and to delineate vessel structures. Different

types of knowledge sources are employed in different systems from various

inputs. One type of knowledge source is the properties of the image

acquisition technique, such as cine-angiography, digital subtraction

angiography (DSA), computer tomography (CT), magnetic resonance

imaging (MRI), and magnetic resonance angiography (MRA). Some

applications utilize a general blood vessel model as a knowledge source.

Stansfield (1986) applied a domain-dependent knowledge of

anatomy to interpret cardiac angiograms in the high-level stages. According

to Stansfield, Anatomical knowledge was embodied within the system in the

form of spatial relations between objects and the expected characteristics of

the objects themselves. Knowledge-based systems exploit a prior knowledge

of the anatomical structure. These systems employed some low-level image

processing algorithms, such as thresholding, thinning, and linking, while

guiding the segmentation process using high-level knowledge. Artificial

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Intelligence-based methods perform well in terms of accuracy, but the

computational complexity was much larger than some other methods.

Rost et al (1998) described their knowledge-base system, called

SOLUTION (Solution for a Learning Configuration System for Image

Processing), and designed to automatically adopt low-level image processing

algorithms to the needs of the application. It aims to overcome the problem of

extensive change requirement in the existing system to perform in a different

environment. The system accepted task descriptions in high-level natural

spoken terms and configures the appropriate sequence of image processing

operators by using expert knowledge formulated explicitly by rules. In this

implementation, extraction process was limited to contours.

Smets et al (1988) and Pallawala et al (2001) had presented a

knowledge-based system for the delineation of blood vessels on subtracted

angiograms. The system encoded general knowledge about appearance of

blood vessels in these images in the form of 11 rules (e.g. that vessel has high

intensity center lines, comprise high intensity regions bordered by parallel

edges etc.). These rules facilitate the formulation of a 4-level hierarchy

(pixels, center lines, bars, segments) each of which was derived from the

preceding level by a subset of the 11 rules.

The main stages in the algorithm were: First, the center lines of the

vessels were obtained by an adaptive maximum intensity detector. Second,

thresholding, thinning, and linking operations were applied to get the final

center line segments. Third, bar-like primitives were constructed using region

growing algorithm on the center lines detected. Finally, bar-like structures

were combined into blood vessel segments using geometrical and topological

knowledge of the blood vessels. They show results of their system that

indicate that the system was successful where the image contains high

contrast between the vessel and the background and that the system had

51

considerable problems at vessel bifurcations and self-occlusions. Walter et al

(2007) used contrast enhancement and shade correction for the extraction of

features like blood vessel and microaneurysms.

2.4.15 Neural Network-Based Approach

Neural networks are used to simulate biological learning and are

widely used in pattern recognition. Neural nets are basically a classification

approach. One of the advantages that make neural networks attractive in

medical image segmentation is their ability to use nonlinear classification

boundaries obtained during the training of the network. Another attractive

feature of the neural nets is the ability to learn. With the selection of a good

training set which includes all possible features or objects, the network can

learn the classification boundaries in its feature space. One of the

disadvantages of neural networks is that they need to train every time a new

feature is introduced in the network. Another limitation is that it is difficult to

debug the performance of the network. The network is a collection of

elementary processors (nodes). Each node takes number of inputs, performs

elementary computations, and generates a single output. Each node is

assigned a weight and the output is a function of weighted sum of the inputs.

These weights are learned through training and then used in the recognition.

Back propagation algorithm is a widely used learning algorithm. One problem

associated with learning is that, learning depends on the training data set. The

size of the training data set affects the learning process. The training

procedure should be rerun each time a new training data is added to the set.

Therefore neural networks require a training data set, the learning process is a

supervised learning. A different class of neural networks is self-teaching and

does not depend on training data set for the learning. The best known of these

classes of neural networks is Kohonen feature maps or Kohonen self-

organizing networks. Neural networks are used in a wide range of

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applications. In medical imaging, neural networks are mainly used as a

classification method where the system is trained with a set of medical images

and the target image is segmented using the trained system.

Nekovei and Sun (1995) described their back-propagation network

for the detection of blood vessels in X-ray angiography. This system did not

extract the vascular structure. Its purpose was to label the pixels as vessel or

non-vessel. The system applied the neural network directly to the angiogram

pixels without prior feature detection. Since angiograms are typically very

large, the network was applied to a small sub window which slides across the

angiogram. The pixels of this sub window were directly fed as input to the

network. Pre-labeled angiograms were used as the training set to fix the

network’s weights. A modified version of the common delta-rule was used to

obtain these weights. The algorithm was compared with two other algorithms,

Bayesian maximum likelihood algorithm and iterative ternary classification

algorithm and the classification accuracy of the three methods were

compared.

Hunter et al (1995) combined neural network-based approach with

knowledge- guided snakes to extract Left Ventricular (LV) boundaries in

Echo cardio graphic images. Their method comprises three stages. In the first

two stages, the neural network-based radial search algorithm detected the

candidate left ventricular edge points along a set of radial search lines which

defined the polar domain. The final boundary extraction took place in this

polar domain. In the third and final stage, the knowledge guided Snakes

automatically select left ventricular boundary points from the candidate edge

points resulting from second stage and form a closed contour. They developed

a new two stage Dynamic Programming method reported to be faster than the

original method in their snake’s implementation. Due to the knowledge-

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guided snakes used in the extraction process, this work can also be classified

as a parametric deformable model approach.

2.4.16 Miscellaneous tube-like object detection Approach

Miscellaneous tube –like object detection approach deals with the

extraction of tubular structures from images. Kompatsiaris et al (2000)

developed a method to detect the boundaries of stents in angiographic images.

Their method first constructed a training set using perspective projection of

various deformations of the 3D wire frame model of the stent. The initial

detection of the stent in the image was accomplished by using the training set

for deriving a multivariate Gaussian density estimate based on Eigen space

decomposition using Principal Component Analysis (PCA). Then, a

maximum likelihood estimation framework was formulated to extract the

stent. In the final stage of the algorithm, a 2D active contour model (snake)

was used to refine the detected stent. The initialization of the snake was

accomplished by an iterative technique considering the geometry of the stent.

This work could also be classified as a parametric deformable model due to

the active snakes used. Fuzzy convergence (Adam Hoover et al 2003)

technique was used to determine the origin of the blood vessel network.

Kaupp et al (1994) presented a method to segment a retinal image

into arteries, veins, the optic disk, the macula, and background. The method

was based upon split-and-merge segmentation, followed by feature based

classification. The features used for classification include region intensity and

shape. The primary goal of this work was vessel measurement; the nerve was

identified only to prevent its inclusion in the measurement of vessels.

The problem of optic nerve detection has rarely received unique

attention. It has been investigated as a precursor for other issues, for example

as identifying a starting point for Blood vessel segmentation (Tamura et al

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1988) and (Tolias, and Panas, 1998). It had also been investigated as a

byproduct to general retinal image segmentation. For instance into separate

identifications of arteries, veins, the nerve, the fovea, and lesions

(Akitaa and Kuga, 1982), (Goldbaum et al 1996) and (Pinz et al 1998).Based

on the above survey, different methods were employed and the performance

was analyzed. The next forthcoming chapters discuss the methods for

exudates detection through optic disk and blood vessel extraction.

2.5 OVERVIEW OF THE WORK

Initially to make the screening process to be automated, the retinal

images were obtained from the fundus camera, keeping this as the input for

the process, the optic disk area was identified. The next step was to trim out

the identified area, defining a circle to completely cover the optic disc. Once

the OD is determined, there was no need for the optic disk to be present in the

image because it may affect the detection of exudates as it had the same

features like intensity, density as that of the exudates. Once the optic disk had

been extracted, abnormalities present in the retina can be easily found. The

final stage was the detection of exudates.

The steps involved in the optic disk include converting the color

image in to gray scale image. The optic disk is the brightest part in the retinal

image and the red component is used for the detection of the optic disk. In the

iteration method, a window of arbitrary size was chosen and it scans the entire

image. When the number of pixels exceeding the threshold value inside the

window becomes greater than a particular value, the entire area under the

window was made white or else it was made as black. The iteration was

repeated by increasing the window size and the output of the first iteration

was fed as input for the next iteration. The final binary image with the white

pixel representing the optic disk can be mapped to the RGB image. In the

clustering, for every pixel exceeding the threshold a box of certain size was

55

constructed and this was the cluster. Many clusters were formed and the

centroid was calculated for each cluster. If the distance between the two

centroids was within the acceptable level, they were joined together to form a

single cluster. The candidate regions were first determined by clustering the

brightest pixels in intensity image. The Principle Component Analysis was

employed in this thesis to the clusters for the detection of the optic disk. The

result of PCA method was kept as input for the proposed method to trace the

circular shape of the optic disk. With the mean centre as the centre and the

mean radius as radius, a circle was drawn and the best fit enclosing the optic

disk was chosen.

The optic disk obtained from the proposed method was made black

and the retinal image with the exudates was left for further detection. By

fixing the threshold, the exudates were extracted. The image to be tested was

kept as the test image. The normal healthy image was kept as the reference

image. The corresponding pixels in both the test and the reference images

were compared. This was done by subtraction and if both pixels have the

same value it gets cancelled. If it differs, the larger value appears in the

output.

The next method used for the extraction was the MDD classifier.

Training images for exudates and background were drawn by drawing small

windows in the exudate patches and background for a number of sample

images. The RGB components were represented in spherical coordinates. The

output of the propagation through radii was converted to spherical

coordinates. The discriminant function for background and exudates were

calculated using their respective means. If the discriminant function of

exudates was lower than discriminant function of background, then the pixel

was classified as exudates or else as background. The procedure was repeated

for all the pixels, and finally on to the color image. The extraction was done

56

using enhanced MDD classifier. For background, a group of pixels that

surround the optic disk were taken. For yellowish region, pixels those

belonging to the optic disk were taken. The discriminant of yellowish region

and background were found. If the discriminant of yellowish region was less

than the background, then the pixel was classified as yellowish region or else

as background. Yellowish region includes optic disk, exudates, drusens,

artifacts etc., To separate exudates, the sharp objects in the image were

identified using sobel operator. As the exudates have desired features of

yellow color and sharp edges, Boolean operation, feature based AND

operations were used. Here the object with sharp objects with sharp edges

selects the objects in the image with yellowish elements. Thus the exudates

were only detected and false detections were removed from the output image.

The presence of exudates can also be done through the extraction of

blood vessels. The blood vessels were extracted by both filter and also by

employing morphological operations. Laplacian and Gaussian operators were

used to detect the blood vessels accurately. The blood vessel edges were

thinned and smoothed for the betterment of the extraction.

The performance measurements for exudate detection using all the

methods were obtained by calculating Peak Signal to Noise Ratio. This was

done by using the formula

PSNR(x, y) = 10*log 10(max (max(x), max(y))) ^2/ |x-y|^2) (2.3)

The values were tabulated and the plot shows that the exudates

detection by using the proposed method gives a higher ratio than the other

methods.

Thus with the automated output, the physician can take decisions

within a few seconds regarding the treatment for the patient. This is very

57

useful in case of medical camps where the eye check up is made for more than

hundreds of people. It is not necessary for the ophthalmologist to visit the

camp, once the fundus camera is fixed at the appropriate angle, the technician

can just take the images of patients and transfer the images to the system, and

it automatically generates the necessary details about the retinal image. If any

abnormalities are present, it quickly detects them and based on the severity,

the patients can be advised to visit the doctor.

Hence, this proves to be more efficient and economical. It is a time

saving process. Also the risk of the patients being identified as normal can be

reduced. Once the retinal image of the patient is fed into the system, our

automated program identifies the Optic Disk, removes it and indicates the

presence of abnormalities. Thus the patients can get a clear idea about the

current status of their eyes.

The presence of exudates can also be found by initially extracting

the blood vessel. The extraction is done by filter method as well as by

morphological method. The green component was separated from the RGB

color retinal image. This is because, the blood vessel appearance will be good

in this component.