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CHAPTER 6

DETECTION OF DIABETIC RETINOPATHY USING ANFIS

6.1 INTRODUCTION

Diabetic retinopathy (DR), a complication of diabetes, is one of the

most significant factors contributing to blindness and so early diagnosis and

timely treatment is particularly important to prevent visual loss. However, if

the symptoms are identified earlier and a proper treatment is provided through

regular screenings, blindness can be avoided. In order to lessen the cost of

these screenings, modern image processing techniques are used to voluntarily

detect the existence of abnormalities in the retinal images. Earliest signs of

diabetic retinopathy are damage to blood vessels in the eye and then the

formation of lesions in the retina. Automatic detection of lesions in retinal

images can assist in early diagnosis and screening of diabetic retinopathy.

Two approaches namely pixel based Color Histogram (CH) technique and

image based anatomical and textural feature extraction are proposed to detect

the presence of exudates, an early occurring lesion in color fundus image.

Extracted features are then fed to ANFIS to classify the images into stages of

DR.

This chapter is organized as follows: Section 6.2 describes the

identification of two anatomical structures namely blood vessels and macula.

Section 6.3 explains the proposed method to detect exudates using pixel based

approach and image based approach. Quantitative analysis of pixel based

approach using color histogram technique is presented in section 6.4 and

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performance analysis of the technique is presented in section 6.5. Section 6.6

describes the feature set used for classification of true bright lesions from

bright non-lesions and section 6.7 describes the performance results of the

classifier. GUI developed for the detection of exudates is explained in section

6.8 and the conclusions are provided in section 6.9.

6.2 DETECTION OF ANATOMICAL STRUCTURES

Detection of the anatomic structures is fundamental to the

subsequent characterization of the normal or disease state that may exist in the

retina. Fundus image analysis system is developed to extract the landmark

features such as retinal blood vessels, macula, OD before identifying

pathological entities such as hard exudates.

6.2.1 Blood Vessel Extraction

Blood vessel in retinal images is a key indicator for diagnosis of

diseases like diabetic retinopathy, hypertension and various vascular

disorders. Reliable methods for segmentation of blood vessels in fundus

images are needed since pathologies may be interpreted as vessels. In order to

solve these problems, multi scale techniques described by Palomera et al

(2010) were used to isolate information about objects by considering

geometrical features at different scales. Blood vessels are extracted using a

combination of eigen values of the image with a multiscale approach. Hessian

matrix describes the second order local image intensity variations around the

selected volumetric pixel (voxel). The partial derivatives are calculated as

voxel intensity differences in the neighborhood of the voxel. Hessian matrix is

computed for each pixel in the image by convolving the original fundus image

R(x, y) with a second derivative of a gaussian kernelG (x, y) with a scale s as

shown in Equation (6.1). From the obtained Hessian matrix, its eigen values

and eigen vectors are calculated.

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L(x, y) = R(x, y) G(x, y; s) (6.1)

whereG (x, y; s) =1

e

L(x, y)istheintensityimage , denotes convolution, and is the

variance. Gradient magnitude of the image intensity is found using the

Equation (6.2) and calculated at different scales since blood vessels appear in

different sizes.

| L| = L + L (6.2)

whereL = I(x, y) sG and = I(x, y) sG

Gx, Gy are the gaussian derivatives in the x and y direction. Lx , Ly

are the first derivative of the magnitude in x and y direction. Local maxima of

the gradient magnitude is calculated using the Equation (6.3).

= max | | ) (6.3)

Large eigen value and small eigen value of the intensity

image are calculated using the Equation (6.4).

=L + L

2 and =L + L

2(6.4)

where = L L + 4L

L , L are the second derivatives of the intensity image in the x

and in the y direction. Local maximum of is calculated using the Equation

(6.5).

= max(s)

s (6.5)

L

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Table 6.1 summarizes the relations between and orientation of a

structure in the image.

Table 6.1 Eigen values of the Hessian matrix and image structure

orientation

1 2 3 Structure OrientationL L L Noise (No Preferred Structure)L L H– Bright Sheet Like StructureL L H+ Dark Sheet Like StructureL H- H- Bright tubular structureL H+ H+ Dark tubular structure

H+ H+ H+ Dark blob-like structureH- H- H- Bright blob-like structure

(H= high, L=low, N= noisy, +/- indicate the sign of the eigen value)

Eigen values play an important role in the discrimination of local

orientation pattern. Eigen vector decomposition extracts an orthonormal

coordinate system that is aligned with the second order structure of the image.

Vessel structures are considered to be a tubular structure. With the resulting

theoretical behavior of the eigen values and knowing the model of the

structure to be detected, the decision can be made, if the analyzed voxel

belongs to the structure being searched. By thresholding the image formed by

the smallest eigen value a complete vessel structure is obtained.

6.2.2 Detection of Macula

Macula is the area of acute vision within the retina. Region of

interest (ROI) is taken over the optic disc boundary. The optic disc boundary

is traced and the diameter of the OD is calculated. Optic disc diameter is

calculated to find macular region since macula lies at a distance of twice the

optic disc diameter. First the macula region roughly estimated using the OD

diameter is shown in Figure 6.1.

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Figure 6.1 Distance between macula and optic disc

To get the desired feature in the image and to remove the uneven

background illumination, top hat filtering technique is used with a disc shaped

structuring element of radius 13.Top-hat filtering computes the morphological

opening of the image and then subtracts the result from the original image as

shown in Figure 6.2. Resulting image is then subtracted from the original

image as in Figure 6.3.

Figure 6.2 Filtered image

Figure 6.3 Subtracted Image Figure 6.4 Detected macula

Macula region appears black due to lower intensity and inverse

binary is performed on the detected image to identify macula as shown in

Figure 6.4.

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6.3 DETECTION OF DIABETIC RETINOPATHY

Color fundus image

Figure 6.5 Flow diagram to detect diabetic retinopathy

Color space conversion ( RGB to L*a*b* )

Fundus region detectionand mask creation

Local contrastenhancement

Color histogramthresholding Feature extraction

Classification usingANFIS

Exudatespresent

Normal

DiabeticRetinopathy

Blood vessel Area

Detection ofcandidate pixels

Performanceevaluation

Optic disc elimination(Pixel based )

(Image based)

Yes

No

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Flow diagram of the proposed fundus image analysis system is

shown in Figure 6.5. Fundus retinal images of RGB color space shown in

Figure 6.6(a) is transformed into L*a*b* color space shown in Figure 6.6(b).

(a) Color fundus image (b) L*a*b*color space

Figure 6.6 Input DR image and color space conversion

6.3.1 Fundus Region Detection

A retinal color fundus image comprises of a circular fundus and a

dark background neighboring the fundus. It is important to detach the fundus

from its background so that the further processing is only carried out for the

fundus and not hindered by pixels belonging to the background. In this sub-

section, a method for creating a binary fundus mask prior to lesion detection

is described. For fundus region detection, initially binarization process is

employed over the fundus color image to convert the L*a*b* color space

image into binary image. It converts the input image to grayscale and then

converts this grayscale image to binary by thresholding. The output image

replaces all pixels in the input image with luminance greater than the value 1

as white and replaces all other pixels with the value 0 as black. Followed by

that, the filter will morphologically close the binary image. Morphological

closing of a binary image is defined as the dilation of the image followed by

the erosion of the dilated image. The closing filter operation smoothens the

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boundaries, reduce small inward bumps, join narrow breaks and fill small

holes caused by noise. In a fundus mask shown in Figure 6.7, pixels

belonging to the fundus are marked with 1’s and the background of the fundus

with 0’s.

Figure 6.7 Fundus mask

With the help of the fundus mask an exudates detection algorithm

can process only the pixels of the fundus and omit the background pixels as

shown in Figure 6.8.

Figure 6.8 Fundus Mask area

Before starting the search of the abnormal lesions from an acquired

fundus image, the image has to be pre-processed to ensure adequate level of

success in the abnormality detection. There is a wide variation in the color of

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the fundus image due to race, iris color and the contrast of retinal images is

not sufficient due to attributes of lesions and decreasing color saturation. The

intensity of a digital image be indexed by (i, j). A small running window W of

size U × U was centered on (i, j) .The objective of this technique is to define a

point transformation dependent on the window such that the distribution is

localized around the mean of the intensity and covers the entire intensity

range. Adaptive Histogram Equalization (AHE) technique, a local contrast

enhancement method developed by (Sinthanayothin 1999) is applied to the

intensity image as in Equation (6.6) to improve both the contrast of bright

lesions and the overall color saturation of the retinal image.

f(i, j) = 255 [ ( ) )]( ) )

(6.6)

where (f) = 1 + exp

fmax and fmin are the maximum and minimum intensity values

within the whole eye image. µW indicates the local window mean and W

indicates standard deviation of the intensity within W.

As a result of this adaptive histogram equalization, the dark area in

that eye image that was badly illuminated has become brighter in the output

eye image while the side that was highly illuminated remains or reduces so

that the whole illumination of the eye image is same. Enhanced image

suppresses the background features and enhances vessel visibility.

6.3.2 Nonlinear Diffusion Segmentation

In this step, the segmentation of lesions is modeled in a framework

that encapsulates the variation in exudates and lesion boundary criteria. The

goal is to localize the lesion boundaries for which nonlinear diffusion

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segmentation is used. The basis of nonlinear diffusion segmentation is the

identification of similar pixels within a region to determine the location of a

boundary. Nonlinear diffusion method proposed by Perona and Malik (1990)

is employed for averting the blurring and localization issues of linear

diffusion filtering. The nonlinear diffusion method applies a non-uniform

process that lowers the diffusivity of those locations with a larger likelihood

to be edges. This likelihood can be calculatedby | | as in Equation (6.7).

u = div(g(| | ) ) (6.7)

u refers to the image, div is the divergence operator, is the

gradient operator. The amount of smoothing can be modulated at each

location by the present magnitude of the gradient g, using the

Equation (6.8).

sm=| | refers to the size of the image gradient.

g(s ) =1

1 + s e(e > 0) (6.8)

The diffusivity function g: s [ 0, 1] is a decreasing function of

either the size of the image gradient or the smoothed gradient. The function

g(s ) detects the presence of an edge at a particular position. If s is small,

there is a minor probability of an edge at that position, and g is close to 1; if,

s is large, the location is likely to belong to an edge, and the value of g will

be close to zero.e is an edge threshold parameter. Segmented image is shown

in Figure 6.9(a) and the region with similar intensity in Figure 6.9(b).

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(a) Segmented image (b) Similar intensity region

Figure 6.9 Region marked with similar intensity

6.3.3 Localization of Optic Disc

Optic disc has to be identified prior to the process of exudates

detection since it appears with similar intensity, color and contrast to other

features on the retinal image. Localization of an optic disc is a vital step in the

automated retinal image screening system. The optic disc is exemplified by

the largest high contrast among circular shape areas. It is noticed to be in oval

shape with an average diameter of 1.5 to 1.7 mm and approximately 3 mm

nasal to the fovea. For this, again the segmented fundus image is converted

into binary image. Regions with high intensity value (exudates and optic disc)

are grouped into white (1 pixel) and the other into regions as black (0 pixel).

Subsequently, to locate the optic disc in the color fundus image, color

histogram equalization technique is then applied independently for each

extracted regions.

Color Histogram (CH) is widely used as an important color feature

indicating the content of the image, due to its robustness to scaling,

orientation, perspective, and occlusion of images. CH is based on the intensity

of the three channels and represents the number of pixels that have colors in

each of a fixed list of color ranges. Given a color space containing B color

bins, the color histogram of a color image with n pixels is represented as

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a vector H = [h0, h1, h2,…..hB-1] in which each entry hi indicates the statistical

figures of the colors in the color image which belong to the ith bin as shown in

Equation (6.9).

h , i = 0, 1….B - 1 (6.9)

where ni is the number of pixels with colors in the ith color bin. Clearly, the

more bins a color histogram contains the more discrimination power it has.

However, a histogram with large number of bins will not only increase the

computational cost, but will also be inappropriate for building efficient

indexes for image data base. The maximum pixel value of color histogram

localized as optic disc is shown in Figure 6.10. In each of the three color

channels 5 color bins are used, resulting in a total of 125 bins. In this method,

to bin the color triplets, each (L*a*b*) triplet is truncated as (L*a*b*) where

each value can only be a multiple of 25 up to a maximum of 255.Triplets are

normalized by the sum of their values. Color difference is then calculated

using the euclidean distance between two color triplets.

Figure 6.10 Optic disc localization

6.3.4 Detection of Soft and Hard Exudates using Color Histogram

(Pixel based approach)

Input: Color fundus image

Output: Segmentation results by color histogram thresholding.

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Once the optic disc is localized from the color fundus image, the

exudates are detected based on the color histogram thresholding. In this

method, spatial information about the colors is incorporated by dividing the

masked image into blocks.

1. Color fundus image is divided into number of non-overlapping

blocks.

2. Mask image MI is created for the original image. Subsequently

MI is split into blocks with block size v x v.

3. Color histogram is calculated for each block of the image.

4. By the use of threshold value based on color histogram, soft

exudates are detected over the color fundus image. The

threshold is chosen in a very tolerant manner to differentiate

between the hard and soft exudates region in a color fundus

image. Finally, based on the chosen threshold value, the soft

and hard exudates are detected from the color fundus retinal

image. Soft exudates pixels shown in Figure 6.11 are detected

when the threshold value is greater than 0.8 and less than 0.85.

Figure 6.11 Detection of soft and hard exudates with optic disc masked

Input images with different DR severity stages are shown in Figure

6.12 and the corresponding detected exudates in Figure 6.13.

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Figure 6.12 DR images

Figure 6.13 Detected exudates

Detected pixels are compared with the ground truth where each

exudates pixels are marked by an expert and it is shown for two images in

Figure 6.14. Number of exudates pixels correctly detected or missed are

identified and from which sensitivity and specificity are calculated. The

method works better than computing histogram over the entire image.

(a) Input image (b) Segmented blood vessels

(c) Detected exudates (d) Ground truth pixels

Figure 6.14 Comparison of detected and ground truth pixels

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6.4 QUANTITATIVE ANALYSIS OF EXUDATES DETECTION

Quantitative results generated from CH technique are used to

diagnose or evaluate the progress of the illness. Pixels detected using the

algorithm are compared with the ophthalmologist’s hand drawn ground truth.

To evaluate the performance, sensitivity, specificity and accuracy are

calculated on a per-pixel basis.

Table 6.2 Quantitative analysis of pixel based approach for exudates

detection

Images

Groundtruthpixels

Detectedexudates TP FP FN TN

Sensitivity(%)

Specificity(%)

Accuracy(%)

1 8431 11428 7997 1052 80 34492 99.01 97.88 98.092 2022 2350 1985 472 18 26148 99.1 99.24 99.233 840 1269 1793 457 20 29498 99.01 99.47 99.444 325 964 916 648 9 29477 99.03 99.74 99.715 1392 2003 3499 592 32 29388 98.87 99.35 99.316 1533 2456 4783 423 43 28342 99.11 98.53 98.617 2043 3243 5686 297 48 29372 99.16 98.3 98.448 631 1283 2892 337 28 29498 99.04 99.2 99.199 1613 2870 3953 350 36 28351 99.1 98.78 98.8210 529 1473 1519 453 13 29496 99.15 98.87 98.8811 507 1369 2458 528 23 29517 99.07 99.23 99.2212 326 1366 792 496 5 30513 99.37 99.71 99.713 1295 2066 1254 384 12 28505 99.32 99.25 99.2614 713 1599 978 435 8 28436 99.19 99.63 99.6215 9127 11957 9012 503 88 28184 99.03 99.28 97.716 1209 1764 1878 586 16 29360 99.16 99.37 99.3617 1189 2075 1362 438 11 27265 99.2 99.57 99.5518 674 1370 1256 352 10 26184 99.21 99.61 99.5919 375 817 372 445 3 29147 99.2 99.88 99.8720 3017 5185 2973 534 30 27498 99 99.16 99.1421 15296 18570 9453 566 94 28437 99.02 97.04 97.5322 3878 5108 3654 478 35 26995 99.05 98.62 98.6723 2571 4109 2953 522 29 27444 99.03 99.2 99.18

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Pixel-based evaluation considers four values, namely true

positive (TP), a number of exudates pixels correctly detected, false positive

(FP), a number of non-exudates pixels which are detected wrongly as exudate

pixels, false negative (FN), a number of exudate pixels that were not detected

and true negative (TN), a number of non-exudates pixels which were correctly

identified as non-exudate pixels. Table 6.2 shows the quantitative result of

TP, FP, FN, TN, sensitivity, specificity and accuracy from the images of

diseased eyes.

6.5 PERFORMANCE ANALYSIS OF EXUDATES DETECTION

The performance of exudates detection using color histogram

technique was evaluated quantitatively by comparing the detected pixels with

ophthalmologist’s hand-drawn ground truth images pixel by pixel. From these

quantities, the sensitivity, specificity and accuracy were computed using the

Equations (6.10), (6.11) and (6.12) respectively. Calculations for the fifth

image in Table 6.2 are shown below.

Sensitivity = TP / (TP + FN) (6.10)

= 3499/ (3499+32)

= 98.87%

Specificity = TN / (TN + FP) (6.11)

= 29388 / (29388+592)

= 99.35%

Accuracy = (TP + TN) / (TP + FP + FN + TN) (6.12)

= (3499+24388) / (3499+592+32+29388)

= 99.31 %

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Color histogram technique when evaluated on 200 real time images

provided an average sensitivity of 99.11%, specificity of 98.32% and an

accuracy of 99.1%.

6.6 IMAGE BASED APPROACH USING ANATOMICAL ANDTEXTURE FEATURES

The segmentation of bright lesions results in a set of candidate

bright lesion objects. The aim of the candidate bright lesion classification

system is to classify the detected objects as either bright lesion or bright non-

lesions. The bright non-lesions false positives are due to the influence of

cluster overlapping and non-uniformity of gray level. These false positives are

also due to the presence of regions having high background brightness. In

order to remove such candidates, classifiers are used which are trained with

the features derived from the candidates.

6.6.1 Feature Extraction

Feature extraction stage refers to pixel characterization by means

of a feature vector and it is a pixel representation in terms of some

quantifiable measurements which may be used in the classification stage to

decide whether pixels belong to a real exudates or not. In order to classify the

segmented regions into exudates and nonexudates, the images must be

represented with relevant and significant features to provide best class

separability.

Texture analysis is used to extract the features of the retina and it is

defined by a set of statistics extracted from the segmented region. Suitable

feature set is extracted from the enhanced retinal images and from the

detected anatomical structures. These anatomical structures include macula,

blood vessels and the optic disc. Direct segmentation methods segmenting DR

are more complex because the texture of unhealthy areas of retina is quite

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irregular. Texture is examined only on the segmented image without

including the background of the image. Features extracted from the

segmented regions for various categories namely normal, mild, moderate and

severe stages are shown in Table 6.3.

Table: 6.3 Feature extraction from real time images

ImagesBlood Vesselarea (value

in pixels)

Area ofcandidateexudatesin pixels

Contrast Correlation Energy Homogeneity Entropy

1 13848 914 1.3876 0.8156 0.8093 0.9739 6.7445

2 15513 626 1.4798 0.8244 0.8165 0.9751 6.7415

3 14198 711 1.3914 0.8136 0.8079 0.9785 7.0132

4 13767 856 1.4321 0.8132 0.8298 0.9718 6.7967

5 17456 813 1.4825 0.8197 0.8053 0.9757 6.7507

6 17866 1057 1.4529 0.8138 0.8067 0.9712 6.7625

7 29259 557 1.2824 0.8367 0.8199 0.9778 6.6593

8 29543 296 1.2537 0.8385 0.8037 0.9759 6.6474

9 30454 456 1.1738 0.8352 0.8174 0.9731 6.5733

10 29623 256 1.2681 0.8319 0.7901 0.9725 6.3801

11 31409 437 1.1036 0.8484 0.8133 0.9714 6.4041

12 33757 355 1.0772 0.8476 0.8141 0.9799 6.6659

13 27212 49 0.6321 0.9324 0.7414 0.9851 5.4236

14 22784 75 0.6021 0.9387 0.7389 0.9804 6.5452

15 24986 58 0.8752 0.9344 0.7456 0.9805 6.4832

16 25437 147 0.6454 0.9351 0.7361 0.9885 6.4734

17 23531 64 0.7252 0.9312 0.7441 0.9807 6.4251

18 24985 72 0.7577 0.9442 0.7412 0.9835 6.4824

19 26660 0 0.3328 0.9634 0.7228 0.9914 5.4236

20 28357 7 0.3334 0.9732 0.7278 0.9927 6.5452

21 27243 4 0.4555 0.9856 0.7264 0.9972 6.4832

22 27321 5 0.4342 0.9838 0.7218 0.9934 6.4734

23 29659 7 0.2422 0.9746 0.7281 0.9949 6.4251

24 28467 2 0.3564 0.9659 0.7315 0.9897 6.4824

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Features extracted related to first order are mean, standard

deviation, entropy, skewness and kurtosis. Cooccurence matrix captures the

spatial distributions of gray level and represents the occurrence rate of a pixel

pair with gray levels i and j. Contrast, correlation, energy, homogeneity and

entropy are the set of features extracted using GLCM. Area occupied by

blood vessels, area occupied by the candidate exudates are the features

extracted from anatomical structures. Features extracted are selected using

SFFS and seven significant features namely area of blood vessels, area

occupied by the candidate exudates, contrast, correlation, energy,

homogeneity and entropy are fed as input to ANFIS. First order features do

not show any discriminatory performance. The data is normalized and the

generated data contains normalized feature vector computed around each

pixel. The feature vector so generated from patterns is assigned to ANFIS forclassification of images.

Graphical representation of features for few images representing

various stages of DR is shown from Figure 6.15 to Figure 6.19.

Figure 6.15 Data distribution of contrast for few images

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Cont

rast

Images

severe

Moderate

Mild

normal

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Figure 6.16 Data distribution of energy for few images

Figure 6.17 Data distribution of homogeneity for few images

0.66

0.68

0.7

0.72

0.74

0.76

0.78

0.8

0.82

0.84

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Ener

gy

Images

severe

Moderate

Mild

normal

0.9550.96

0.9650.97

0.9750.98

0.9850.99

0.9951

1.005

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Hom

ogen

eity

Images

severe

Moderate

Mild

normal

172

Figure 6.18 Data distribution of entropy for few images

Figure 6.19 Data distribution of correlation for few images

0

1

2

3

4

5

6

7

8

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Entr

opy

Images

severe

Moderate

Mild

normal

0

0.2

0.4

0.6

0.8

1

1.2

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Corr

elat

ion

Images

severe

Moderate

Mild

normal

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Figure 6.20 Data distribution of blood vessels for few images

Contrast, correlation, energy, homogeneity, entropy, area occupied

by the blood vessels and area occupied by the candidate exudates were the

features extracted from the fundus images. Contrast feature shown in Figure

6.15 have different values for normal, mild, moderate and severe stages of

diabetic retinopathy and hence gives a clear differentiation of the classes.

Contrast provides a low value for normal images and a value

greater than 1 for moderate and severe stages due to the leakage of blood

vessels in the retina. There is a overlapping of normal and mild stages using

energy feature. Energy feature shown in Figure 6.16 provides a less

discriminative effect in classifying the moderate and severe stages.

Homogeneity values in Figure 6.17 gives a clear differentiation between the

moderate and severe stages but cannot effectively differentiate the initial stage

of the disease from the normal stage. Entropy values in Figure 6.18 and

correlation values in Figure 6.19 ranks last in identifying the progress of the

disease.

0

5000

10000

15000

20000

25000

30000

35000

40000

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Bloo

d ve

ssel

are

a(in

pix

els)

Images

severe

Moderate

Mild

normal

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Figure 6.20 describes the blood vessel area occupied by the pixels

in various stages of the disease. In the mild stage of DR, blood vessel area

will be less when compared to normal images and there will be a few

exudates. Also the distance between macula and the bright lesions will be

greater than one disc diameter.

In moderate stage there will be more number of exudates with less

blood vessels compared to the mild stage and the distance between macula

and exudates is less than one disc diameter. In the severe stage of the disease,

exudate occupies a large area due to leakage of blood from the vessels and the

distance between exudates and macula is still less compared to moderate

stage. To analyze the severity of the disease, the polar coordinate system

shown in Figure 6.21 is overlaid on the input image to analyze the distribution

of exudates around the fovea. If the exudates are in fovea region, then it is a

severe stage of DR leading to complete blindness.

Figure 6.21 Polar coordinate system

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6.7 ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM AS A

CLASSIFIER

Neuro-fuzzy systems have the potential to capture the benefits of

both neural networks and fuzzy logic in a single framework. In ANFIS, the

membership functions are extracted from a data set that describes the system

behavior. ANFIS learns features in the dataset and adjusts the system

parameters according to a given criterion. Set of features extracted from first

and second order statistics were used as inputs to ANFIS and the performance

was evaluated.

Using ANFIS, 90 nodes with 40 linear and 70 nonlinear parameters

are formed with five fuzzy rules. Step size for parameter adaptation had an

initial value of 0.01. The system is loaded with the statistical features of the

fundus images along with the desired output from the workspace for training

the network. In this work, training and testing set were formed by 165 and

250 data. 80 normal and 85 abnormal images were used for training and 120

normal and 130 abnormal images were used for testing DR. Abnormal images

include 30 mild, 50 moderate and 50 severe DR images. Dataset used for the

classification is shown in Table 6.4. Schematic of the ANFIS structure

obtained for the proposed system is shown in Figure 6.22.

Table 6.4 Dataset used for classification of DR images

Images Training Data Testing Data No. of Images/Class

Normal 80 120 200

Abnormal 85 130 215

Total no of images 165 250 415

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Figure 6.22 ANFIS structure for DR detection

ANFIS is initialized with 100 iterations and 0.01error tolerance.

Step size for parameter adaptation is 0.1. The leftmost node in Figure 6.22 is

the input node. Training data produced a fuzzy inference system which

contains five rules. Each input was given five membership functions and the

output was represented with two linear membership functions. The branches

in Figure 6.22 are color coded to indicate whether or not and, not or or are

used in the rules. If then rules generated from ANFIS is shown in Figure 6.23.

Blue colored nodes indicates AND operation.

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Contingency table for detection of exudates is shown in Table 6.5.

Table 6.5 Contingency table for exudates detection

Exudates Present Exudates AbsentExudates detected True Positive (TP) False Positive (FP)

Exudates not detected False negative (FN) True negative (TN)

Dataset used for neural network based back propagation and

ANFIS are shown in Table 6.6. Seven features are used as inputs to ANFIS

classifier for detecting the presence or absence of exudates in the retinal

images. Here, the network is trained to identify two classes viz normal and

abnormal. Comparative analysis is performed between ANFIS and back

propagation neural network classifier. Classifiers are compared based on

correctly classified images, misclassified images and classification accuracy.

Table 6.6 Classifier results for exudates detection

Stage TestImages

ANFIS Back propagation

CCI MI CA(%) CCI MI CA(%)

Normal 120 118 2 98.3 115 5 95.8

Abnormal 130 130 - 100 126 4 96.9

Overall accuracy of ANFIS with image based approach is 99.2%

with a sensitivity of 100% and a specificity of 98.3 %. For back propagation,

the accuracy is 96.4% with a specificity of 95.8% and sensitivity of 96.9%.

Root mean square error generated for ANFIS is 0.1195 and for back

propagation it is 0.496. Back propagation is computationally heavy and takes

a longer time for its convergence. As the epochs increases convergence time

also increases and high epoch is required to get the desired result in neural

networks. In the case of ANFIS, accurate result is obtained at a lesser epoch

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with a reduced convergence time. In addition, the algorithm performance is

also measured with Receiver Operating Characteristic (ROC) curve shown in

Figure 6.24.

ROC is a graphical plot of the sensitivity or true positives against

(1 specificity) or false positives by varying the threshold on the probability

map. ROC can also be plotted by the fraction of true positives known as the

True Positive Rate (TPR) against the fraction of false positives known as the

False Positive Rate (FPR). The area under the ROC is 0.99 which contributes

towards better performance of the system. As true positive is closer to 1 and

false positive closer to 0 it can be seen in Figure 6.24 that the proposed

algorithm is effective in detecting DR.

Figure 6.24 Receiver operating characteristic curve

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Table 6.7 Comparison of performance measure for the proposed

method and related works.

TechniquePixel based approach Image based approach

Sensitivity(%)

Specificity(%)

Sensitivity(%)

Specificity(%)

Morphologicalreconstruction

92.8 92.4 100 94.6

Color features - - 100 70Recursive regiongrowing

88.5 99.7 - -

Anatomical andtexturefeatures(Proposed)

99.11 98.32 100 98.3

A comparison of the proposed method with few techniques using

pixel and image based approaches based on specialized features are shown in

Table 6.7.Gray level variation and morphological reconstruction techniques

described by Walter et al (2002) detected exudates with 92.8% sensitivity,

92.4% predictive value in a pixel based approach and achieved 100%

sensitivity 70% specificity using image based approach for 30 images. This

approach cannot be used to detect soft exudates since the processing step

requires more number of parameters. Improper selection of threshold leads to

a decrease in sensitivity and specificity. Wang et al (2000) used color features

with a statistical classification and achieved 100% sensitivity and 70%

specificity for 154 images. Brightness adjustment procedure is required to

distinguish exudates from the background color near the disc. These

techniques are highly sensitive to image contrast. Sinthanayothin et al (2002)

used recursive region growing segmentation to detect exudates and reported

88.5% sensitivity for 30 images. Selecting seed points are difficult in this

technique. Experimental results shows that careful preprocessing, anatomical

and textural features and appropriate classifier together provide excellent

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exudates detection performance even on low quality images. 99.11%

sensitivity and 98.32% specificity is achieved in pixel based approach using

color histogram and 100% sensitivity and 98.3% specificity using anatomical

and texture features.

6.8 GRAPHICAL USER INTERFACE

Graphical User Interface (GUI) is developed to provide

ophthalmologists with the information about blood vessels, and exudates. The

technique serves as a novel integrated platform for fundus image analysis

applicable to a clinical setting and can be used for disease progression. GUI

developed for an abnormal DR image is shown in Figure 6.25 and Figure 6.26

describes a GUI for a mild DR image.

Figure 6.25 GUI for an abnormal DR image

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Figure 6.26 GUI for a mild DR image

6.9 SUMMARY

A novel system for the automatic detection of anatomical structures

such as blood vessels, macula and optic disc in fundus images has been

presented in this chapter. As a large variation in the fundus color is seen

among different subjects, color information used in the preprocessing stage

serves as an important feature to distinguish among the exudates and non

exudates pixels. Careful masking of the optic disc helps in the identification

of even the faint exudates near the disc. Vessel enhancement step paves way

to identify the exudates even at the vessel end. In this analysis, the input

features are based on the specific characteristics of the exudates like color and

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texture. Texture features are relatively correlated but exhibits discriminatory

character for each of the images. Quantitative results generated using color

histogram technique achieves 99.11% sensitivity, 98.32% specificity and

indicates that false positives are few in this approach.

It can be observed that the features detected using structure and

texture can be used as a supporting tool for the diagnosis of DR. Accuracy of

the system has been evaluated on a database of 250 images. The proposed

fundus image analysis system works better in identifying the exudates with a

sensitivity of 100% and specificity of 98.3% for image based approach. The

algorithm used to detect exudates using anatomical and textural features are

reliable and effective since the true positive fraction is high and the false

positive rate obtained is low. Compared with the published methods, proposed

method provides high specificity and accuracy. The results are encouraging

and these methods contribute to the overall development for the screening of

DR in medical camps and helps clinicians to diagnose or evaluate the progress

of the illness.