Binary operation based hard exudate detection and fuzzy based classification in diabetic retinal fundus images for real time diagnosis applications

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 10, No. 3, June 2020, pp. 2305~2312
ISSN: 2088-8708, DOI: 10.11591/ijece.v10i3.pp2305-2312  2305
Journal homepage: http://ijece.iaescore.com/index.php/IJECE
Binary operation based hard exudate detection and fuzzy based
classification in diabetic retinal fundus images for real time
diagnosis applications
Arun Pradeep1
, X. Felix Joseph2
1
Department of Electrionics and Communication Engineering, Noorul Islam University, India
2
Department of Electrical and Electronics Engineering, NICHE and Bulehora University, Ethiopia
Article Info ABSTRACT
Article history:
Received May 2, 2019
Revised Nov 22, 2019
Accepted Nov 30, 2019
Diabetic retinopathy (DR) is one of the most considerable reasons for visual
impairment. The main objective of this paper is to automatically detect and
recognize DR lesions like hard exudates, as it helps in diagnosing and
screening of the disease. Here, binary operation based image processing for
detecting lesions and fuzzy logic based extraction of hard exudates on
diabetic retinal images are discused. In the initial stage, the binary operations
are used to identify the exudates. Similarly, the RGB channel space of
the DR image is used to create fuzzy sets and membership functions for
extracting the exudates. The membership directives obtained from the fuzzy
rule set are used to detect the grade of exudates. In order to evaluate
the proposed approach, experiment tests are carriedout on various set of
images and the results are verified. From the experiment results,
the sensitivity obtained is 98.10%, specificity is 96.96% and accuracy is
98.2%. These results suggest that the proposed method could be a diagnostic
aid for ophthalmologists in the screening for DR.
Keywords:
Binary operations
Diabetic retinal image
Fuzzy logic
Hard exudates
Image processing
Copyright © 2020 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Arun Pradeep,
Department of Electronics and Communication Engineering,
Noorul Islam University,
Thuckalay, Kanyakumari District, Tamil Nadu, India.
Email: arunpradeep@msn.com
1. INTRODUCTION
Diabetic Retinopathy (DR), which is the real reason for visual deficiency, could be backed off by
early recognition. Programmed screening frameworks help in recognizing the essential manifestations like
exudates, which are because of the spillage of lipids from blood vessels. These lipids look like yellow shade
on the retinal images. A skillful pre-processing of the images is essential for managing, magnificence and
separation of assortments. A set of optimally adjusted morphological operators for exudate recognition on
diabetic retinopathy patients’ was proposed by Sopharak et.al. [1]. Ophthalmologists' hand-drawn ground-
truths are used for comparison and validation of detected exudates. Sanchez et.al. [2, 3] presented an
automatic image processing algorithm for the detection of hard exudates. In this work, the authors used
a database containing 58 retinal images with different combinations of colour, brightness, and quality.
The computational intelligence techniques are also used for the automated identification of exudate
pathologies in retinopathy images [4]. The technique employs a dataset with 300 retinal images and
the technique achieved 96% sensitivity. A morphology mean shift algorithm based approach for the detection
of exudate lesions in color retinal images was introduced by Wisaeng et.al. [5]. The Mathematical
Morphology Algorithm (MMA) is the key element in this method. Multiscale Amplitude-Modulation-
Frequency-Modulation (AM-FM) methods are useful for discriminating between the normal and pathological
retinal images [6]. An automated diabetic retinopathy diagnosis system useful for the detection of various

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Int J Elec & Comp Eng, Vol. 10, No. 3, June 2020 : 2305 - 2312
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lesions of the retina including exudates, microaneurysms and hemorrhages get wide acceptance [7].
Many authors tried to develop more efficient DR detecting schemes [8-14].
To further enhance the detection accuracy, a combination of 3 methods, viz. Image binarization,
Region of Interest (ROI) based segmentation and Morphological Reconstruction (MR) are proposed. [15].
Nidhal and Enas [16] developed a method for localizing, isolating the optic disk and then detecting
the exudates. For localizing the optic disk, and treating the similarity between exudates and optic disk, they
developed a new algorithm.
Inorder to improve the detection, gradient controlled fuzzy c means alsorithm are proposed [17].
For reducing the noise content in the original image, use of smoothening filters is also proposed in this work.
Carla et.al. [18] presented an automated system for detection of exudates in the macula. This approach uses
optimal thresholding of Instantaneous Amplitude (IA) components. Here, the IA components are extracted
from multiple frequency scales for generating candidate exudate regions. [18]. New pre-processing methods
which perform normalization, denoising, detect reflections and artifacts in the image were developed by
Zhang et.al. [19]. In this work, authors developed a candidate’s segmentation method which is based on
mathematical morphology. Harangi and Hajdua [20] presented a way for automatic segmentation of exudates
consisting of a candidate extraction step. Here, the extraction of possible exudate candidates is done by
the use of grayscale morphology and their proper shape is determined by a Markovian segmentation model.
A non-linear background elimination strategy for improved automatic exudates detection in retinal images
was introduced by Akter et.al. [21]. Similar studies can be extensively found in literature [22-24].
For the computer assisted recognition, a hybrid algorithm using Migrating Bird Optimisation and Support
Vector Machine (MB-SVM) classifiers has been proposed [25]. A four steps strategy for the detection was
devoleped by Elbalaoui and Fakir [26]. It includes shifting the colour space, Optic Disc (OD) elimination,
exudates segmentation and finally separation of exudates from the background.
2. RESEARCH METHOD
This paper proposes an algorithm which combines two different principles for detecting and
extracting hard exudates from the given retinal images; they are binary operation and fuzzy logic. At first,
mathematical binary operations are used to identify the exudates after pre-processing and optic disc
elimination. The detail is shown in Figure 1.
Figure 1. Proposed method
During pre-processing, the RGB color image is converted into HSI color space. Then the noise
occurred in the image is eliminated by applying median filter over the intensity band. Following this noise
removal, Contrast Limited Adaptive Histogram Equalization (CLAHE) method is applied to enhance
the image based on the contrast value. This will prevent the saturation of the similar areas in the image. Some
of the earlier research works used Gaussian function for noise removal. Figure 2 shows the pre-processing
steps. The color conversion from RGB to HSI and I-band of the images are used for next level processing.
If the intensity of exudates and optic disc are high, then the RGB color image is converted into HSI and
proceded for further processing. The conversion RGB to HSI is a direct work and it doesn’t need
a conversion from RGB to grayscale. In order to eliminate the optic disc, it is considered that the optic disc is
a component with largest circular shape that exists in the fundus images. In the second process the exudates
are classified as soft and hard using fuzzy logic rules. The fuzzy rules are created using fuzzy sets obtained
from the RGB fundus images. The fuzzy rules determine the availability of the hard exudates in the diabetic
retinal images.

Int J Elec & Comp Eng ISSN: 2088-8708 
Binary operation based hard exudate detection and fuzzy based classification in diabetic ... (A. Pradeep)
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There are some imperfections in binary images. Binary regions of the images can be produced by
certain level of threshold values and are biased by texture and noise. Binary operations follow the objectives
of eliminating the imperfections by considering them to form a structure of the image. It can be obtained
from HSI image.
Figure 2. Result obtained from preprocessing stage
2.1. Optic disc elimination
Opening, closing, and erosion are some of the operations involved under binary and morphological
operations. In this paper the closing operation is applied to get the structure of the disc on the input image I.
After closing operation, a simple threshold method is applied to get a binary image. The output image 𝛀
comprises of various connected regions such as 𝑪𝒊 and it can be represented as:
Ω = ⋃ 𝐶 𝑘, 𝐶𝑖 ⋂ 𝐶𝑗 = 0𝑘 ∈𝑚 , ∀ 𝑖, 𝑗 ∈ 𝑚, 𝑖 ≠ 𝑗 (1)
Where, 𝒎 changed from 𝟏 𝒕𝒐 𝒌, 𝒌 is the total number of components connected in the image. Comparing
with the background, all the components are having more number of pixels and forms a circular shape that
belongs to 𝑪𝒊. Optic disc is also included in 𝑪𝒊. Hence separating the component (optic disc) from 𝑪𝒊 is very
easy and can be extracted effectively from all the other structures in the retinal image. The biggest region
based component is called as 𝑹𝒊 available in 𝑪𝒊, where the compactness of 𝑹𝒊 can be measured using
the following formula as:
𝐶(𝑅𝑖) = 4𝜋
𝐴(𝑅 𝑖)
𝑃2(𝑅 𝑖)
(2)
Where, the number of pixels in the 𝒊 𝒕𝒉
region is denoted as 𝑨(𝑹𝒊) and the pixels out of the region (𝑹𝒊) is
𝑷(𝑹𝒊). In this paper in order to increase the efficiency, already proven methods such as P-tile method [16]
and Nilblack’s method [17, 18] are taken as the thresholding methods. The optic disc is considered as
the largest connected component where it provides the highest values of compactness among these two
methods. Since exudates and optic disc are having similar color and intensity, it is necessary to eliminate
the optic disc to separate the exudates. Figure 3 shows the output of the image after applying the binary
operation (closing) as OD eliminated including blood vessels.
OD is identified by the largest circular shape available on the connected components.
In the Nilblack’s method, 1.3 is considered as the weight factor. The region containing the optic disc is
brighter than the other regions of the retinal image. From the entire image, the OD comprises of 2% of
brightness and it is used to obtain the binary images which are shown in the output. The output shown in
Figure 3 illustrates the binary operation based results. In order to isolate the optic disc, the Circular Hough
Transformation (CHT) method is used.

 ISSN: 2088-8708
2308
Figure 3. Optic disc elimination
2.2. Detection of exudates
After OD detection and elimination, further process of the proposed system is to identify and extract
the hard exudates. For the direct detection of the hard exudates, binary closing operation with flat disc shaped
structuring element using a 16-pixels radius is employed. This closing operation is applied after thresholding
method. This operation includes the same level of contrast component blood vessels also. So, the standard
deviation of the image is computed. In order to differentiate the components a triangle thresholding method is
applied to detect the very brighter region in the image. From that, unwanted regions are removed by dilation
operation from the thresholding image. After that, flood filling operation is applied on the holes to make
the image ready for reconstruction from the binary operation. Finally, the difference image is taken between
the output image and the intensity based original image (which is obtained from thresholding). The result of
this threshold image is super-imposed on the real RGB image for extracting the exudates.
I3(x) =
1
N−1
∑ (I2(i) − I3(x)̅̅̅̅̅̅)
2
i∈W(x) (3)
Where, W(x) represents the pixels available in the sub-window, 𝑵 represents the total number of pixels in
W(x) and 𝐈 𝟑(𝐱)̅̅̅̅̅̅̅ represents the mean value of the image 𝐈 𝟑(𝐱) and 𝐈 𝟑 represents the local contrast image.
2.3. Fuzzy logic based hard exudate classification- creating fuzzy rules and membership functions
Considering a collection of objects represented as 𝑿, then a fuzzy set 𝑨 is over 𝑿which is defined as
the set of all ordered pairs 𝑨 = {(𝒙, 𝝁 𝑨(𝒙))|𝒙 ∈ 𝑿} , where𝝁 𝑨(𝒙)is called as the set of membership functions
related to the fuzzy set 𝑨. The membership function automatically maps all the elements in the X to
a membership degree among 0 to 1 such that 𝝁 𝑨: X→ [𝟎, 𝟏].
Fuzzy Set: The set of all support of a fuzzy set 𝑨 represents all the points including non-zero membership
degree in 𝑨 such that support (𝑨) = {𝒙 ∈ 𝑿| 𝝁 𝑨(𝒙) > 0}.
Defuzzification: The de-fuzzified values 𝒙∗
is also called as crisp values and it can be represented by,
𝑥∗
=
∫ 𝑥𝜇 𝑖(𝑥)𝑑𝑥𝑠
∫ 𝜇 𝑖(𝑥)𝑑𝑥𝑠
(4)

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Where 𝒔 is represented as the support of the aggregated membership function 𝝁𝒊(𝒙).
The final and main process of the proposed approach is identifying the hard exudates using fuzzy
logic. In order to create fuzzy set, the fuzzy membership function is obtained by RGB color channel of
the retinal image. The three values representing the three different color channels which are represented as
𝑥 𝑟,𝑥 𝑔 and 𝑥 𝑏 are fed as input values into Fuzzy Interference System (FIS). In order to get the output for 𝑥 𝑟,𝑥 𝑔
and 𝑥 𝑏 with respect to the fuzzy rules, the fuzzy interference system gives a degree of membership to
the output variable as 𝑋 𝑜𝑢𝑡. The above described crisp values are obtained according to a centroid method
used to compute the output. All the variables𝑥 𝑟,𝑥 𝑔,𝑥 𝑏 and 𝑋 𝑜𝑢𝑡 are represented using the Gaussian member
function as:
𝜇(𝑥) = 𝑒
−((𝐶 𝑖−𝑥)2(2𝜎𝑖2))
(5)
There are five fuzzy sets which are derived for representing the output variables. The fuzzy result is
counted on all the image elements in each exudate in the retinal images. The area of the hard exudates is
determined by an average fuzzy value as>0.25. The output is also calculated in accordance to the percentage
of the hard exudate’s area. The fuzzy rules are mainly used for classifying the hard exudates. The crisp values
of each hard exudate are calculated for classifying it. If the crisp value should be>0.25, then it is used to
determine the hard exudates. Fuzzy output lesser than 0.25 is considered as soft or tiny exudates.
3. RESULTS AND ANALYSIS
The fuzzy rules and all the discussed procedure were implemented in MATLAB and results are
verified. In this proposed method, dataset named DIARETDB0 and DIARETDB1 are used for implementing
and testing. All the exudates are classified using crips value. For getting the accurate classification, hard
exudates are used. Figure 4 represents the tiny sized hard exudates extracted. The entire data set is divided
into two sub sets as training set and testing set. The features of extracted image are used for cross validation
and to find out the misclassification.
In this paper 75 images are taken from the dataset DIARETDB0 for experiment and the total
numbers of correctly classified images are counted. The soft, hard exudates are classified by more
experiments and comparing the results of the proposed approach delivers a result given below in Table 1.
The performance can be evaluated by calculating the metrics True Positive Rate (TPR) and False Positive
Rate (FPR). The TPR is the ratio of number of normal images identified to the total number of normal image
and FPR is the ratio of number of wrongly obtained classifications to the total number of image to be
classified. The accuracy, sensitivity and specificity are found out based on the formulas as given below:
Accuracy =
True Negatives(TN)+True Positives(TP)
True positives(TP)+False Positives(FP)+True Negatives(TN)+False Negatives(FN)
(6)
Sensitivity =
TP
TP+FN
(7)
Specificity =
TN
TN+FP
(8)
Figure 4. Exudates detection

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The total number of input images experimented and number of images classified by the proposed
approach is given in the following Table 1. The total number of images is 75, in that, 50 images are having
soft exudates and 25 images are having hard exudates. In order to obtain better results four-fold cross
validation is applied and the obtained result is shown in Table 2. Detecting and differentiating exudates as
soft, hard exudates are a hard task, and the obtained TPR, FPR values are comparatively better than
the existing system. Also the efficacy of the proposed method can be analyzed using sensitivity and
specificity metrics. Since the sensitivity and the specificity are comparatively better than the existing
approaches, it is decided that the proposed approach is efficient. Also the performance of the proposed
approach is evaluated by comparing it with the existing systems.
Table 1. Exudate Versus Non-Exudate Comparison Output
Disease Fold-1 Fold-2 Fold-3 Fold-4
Exudates 85.7 100.0 88.5 92.6
Non-Exudates 12.9 10.0 6.1 9.4
Table 2. Performance Analysis in Terms of Sensitivity, Specificity and Accuracy
Measures Fold-1 Fold-2 Fold-3 Fold-4
Sensitivity 86.21 95.12 98.10 98.10
Specificity 87.0 98.10 96.96 96.96
Accuracy 86.66 96.66 98.33 98.33
A large value of specificity means, the proposed algorithm does not extract the wrong portion as
exudates and a low value represents the proposed algorithm is not efficient for extracting the exudates
accurately. To determine the accurate exudates, the average crisp values are utilized by the fuzzy logic.
The proposed approach extracts the hard exudate when the crisp value is>0.09. It promised that the proposed
approach doesn’t classify the soft exudates. In order to compute the performance of the proposed approach,
Receiver Operator Characteristics (ROC) is generated. Here, an intermediate interval, [0, 1] is taken for
locating the pixels which are potential true-negative regions. Figure 5 shows the plot of ROC analysis for
the results obtained from real time experiment.
Only few images are taken from the real time image dataset since processing the entire dataset takes
long execution time. Therefore, out of 200 images, 30 images are used in training process and the remaining
170 images are used for testing process. To evaluate the performance in real time clinical images, the testing
images are collected from Dr.Bhejan Singh Eye Hospital, Nagarcoil, Tamilnadu. The best operating point on
this curve is at a sensitivity of 98.10% at a specificity of 96.96%.
Figure 5. ROC curve for testing set of exudates at image level

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4. CONCLUSION
Identification, detection, extraction and classification of hard exudates from diabetic retinal image
are done. For this purpose, the image is preprocessed, normalized, detected, extracted and classified. It can be
obtained through color conversion, median filtering, binary operations and fuzzy logic. Comparing with other
methods the accurate determination of hard exudates is done by fuzzy rules on the retinal image data. In this
paper a sequence of image processing steps with fuzzy rules is applied to determine the size and intensity of
the exudates. The output is automatically mapped by the Fuzzy Interference System. The image intensity and
size (area) are the parameters used to compute the exudates and a threshold based adjustment is done by
a ROC analysis. The obtained results and the ROC analysis depicts that the proposed approach is more
efficient. The obtained sensitivity is 98.10%, Specificity is 96.96% and Accuracy is 98.2%. These results
suggest that the proposed method could be a diagnostic aid for ophthalmologists in the screening for DR.
In future the proposed work is extended to identifying the other pathological information such as occlusion,
hemorrhages, macula edema and red spots.
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BIOGRAPHIES OF AUTHORS
Arun Pradeep is a Research Scholar at Noorul Islam University, Tamil Nadu. He received his
B.Tech in Electronics and Communication Engg. in 2009 and M.tech in Embedded Systems in
2012. He was an Assistant professor in Electronics and Communication Engineering. at Axis
College of Engineering, Thrissur, Kerala during the period 2012 to 2018. His research interest field
is Medical Image processing.
X. Felix Joseph is an Assistant Professor at Bule hora University, Ethiopia. Dr. Felix Joseph has
more than 14 years of experience in the teaching field in the area of Electrical and Electronics, He
has attended more than 60 short term training programs including two weeks STTP, one week
seminar,one day workshops, and two weeks training programs. Currently he is guiding seven
research scholars and published more than 10 papers in international journals and conferences. His
areas of interests are soft computing applications in digital signal processing, digital image
processing and power electronics. His favorite subject is neural network, which is one of the tool
used in soft computing techniques.

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