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International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING &
ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME

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ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
Volume 4, Issue 6, November - December (2013), pp. 01-08
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DETECTION AND CLASSIFICATION OF NON PROLIFERATIVE
DIABETIC RETINOPATHY STAGES USING MORPHOLOGICAL
OPERATIONS AND SVM CLASSIFIER
Preethi N Patil1, G. G. Rajput1
1

Dept. of Computer Science, Gulbarga University, Gulbarga, Karnataka, India

ABSTRACT
Diabetic retinopathy (DR) is one of the complications of diabetes mellitus that is considered
as the major cause of vision loss among people around the world. The most important signs of
diabetic retinopathy are dark lesions (i.e. microaneurysms and hemorrhages) and bright lesions (i.e.
hard exudates and cotton wool spots). In this paper, we present an efficient method to grade the
severity of DR in retinal images. Morphological operations are used to detect the pathologies
associated with DR, namely, blood vessels, microaneurysms and hard exudates. SVM classifier is
used to grade the retinal image under the categories of Non Proliferative DR (NPDR) namely, normal
(no DR), mild NPDR, moderate NPDR and severe NPDR. The proposed method successfully
classified the subjects into normal, mild NPDR, moderate NPDR and severe NPDR with an accuracy
of 100%, 93.33%, 100% and 86.67% respectively. An average sensitivity of 96.08% and an average
specificity of 97.92% is reported.
Keywords: Diabetic Retinopathy, Hard Exudates, Microaneurysms, Optic Disc, Morphological
Operations, Canny Edge Detector.
1. INTRODUCTION
Diabetes mellitus is a growing health problem in developing countries. According to the
Diabetes Atlas, India with 40.9 million people with diabetes has already become the 'Diabetes Capital
of the World' and this number is set to increase to 69.9 million by 2025 [1]. The prevalence of
diabetes is growing rapidly in both urban and rural areas in India. In 1972, the prevalence of diabetes
in urban areas was 2.1% [2] and this has rapidly climbed to 12-16% representing a 600-800% increase
in prevalence rates over a 30 year period [3, 4]. Till recently, the prevalence of diabetes was reported
1


to be low in rural areas, but recent studies suggest that the prevalence rate is rapidly increasing even
in rural areas, [5] similar to the situation seen in developed countries of the world.
Diabetic retinopathy is one of the complications of diabetes mellitus that occurs when blood
vessels in the retina change. Sometimes these vessels swell and leak fluid or even close off
completely. In other cases, abnormal new blood vessels grow on the surface of the retina. Diabetic
retinopathy usually affects both eyes. People who have diabetic retinopathy often don't notice
changes in their vision in the disease's early stages. But as it progresses, acute vision loss occurs,
making it the primary cause of blindness.
There are two types of diabetic retinopathy, namely, Non-proliferative diabetic retinopathy
(NPDR) and Proliferative diabetic retinopathy (PDR).
NPDR is the earliest stage of diabetic retinopathy. With this condition, damaged blood vessels
in the retina begin to leak extra fluid and small amounts of blood into the eye. Sometimes, deposits of
cholesterol or other fats from the blood may leak into the retina. NPDR can cause changes in the eye,
including:
•
•
•

Microaneurysms-small bulges in blood vessels of the retina that often leak fluid.
Retinal hemorrhages - tiny spots of blood that leak into the retina.
Hard exudates - deposits of cholesterol or other fats from the blood that have leaked into the
retina.

PDR mainly occurs when many of the blood vessels in the retina close, preventing enough
blood flow. In an attempt to supply blood to the area where the original vessels closed, the retina
responds by growing new blood vessels.
There are many algorithms and techniques available in the literature for the detection of
pathologies causing Diabetic retinopathy. The state of art methods have been described below.
U R Acharya et. al. [7] have presented a method of classifying the DR stages by computing
the area of four features, namely, blood vessels, hemorrhages, microaneurysms and exudates.
Gardner, G Keatting, willamson, T and Elliott, A [8] used neural network to detect diabetic features,
namely, vessels, exudates and hemorrhages in fundus images and compared the performance with an
ophthalmologist screening method. Blood vessels, exudates, and hemorrhages were detected with
accuracy rates of 91.7%, 93.1% and 73.8%, respectively. Niemeijer et al. [9] presented a method to
detect red lesions based on hybrid approach. They were able to detect the red lesions with a
sensitivity of 100% and a specificity of 87%. Nayak et al[10] have classified the fundus images into
normal, NPDR and PDR classes with an accuracy of 93%. Acharya U. R [11] used SVM classifier to
categories the fundus images into normal, mild, moderate, severe DDR and PDR. Akara
Sopharak[12] presented an automatic microaneurysm detection from non-dilated Diabetic
Retinopathy retinal images using mathematical morphology. They were able to detect the
microaneurysms with 81.61% sensitivity and 99.99% specificity. Li et al. and O. Chutatape,[13]
proposed a method that divides the image into 64 sub-images followed by application of region
growing and edge detection to detect exudates. C. I. Sanchez, M. Garcia, A. Mayo, M. Lopez and R.
Hornero,[14] proposed a method based on mixture models to separate exudates from background
followed by edge detection technique to distinguish hard exudates from soft exudates. Garcia et al.,
[15] proposed a combination of local and global thresholding to segment exudates followed by
investigating three neural network classifiers to classify exudates. Akara Sopharaket. al.[16] have
tuned the morphological operations to detect the exudates with a sensitivity and specificity of 80%
and 99.4% respectively. Using back propagation neural network, exudates detection is presented in
[17]. A comparative study on machine learning and traditional approaches for exudates detection is
2


presented in [18]. Applying fuzzy C-means clustering techniques to low contrast fundus images with
non-dilated pupils, Akara Sopharak et. al. [19] have reported exudates detection with 92.18%
sensitivity and 91.52% specificity. To detect the DR from fundus images, it is essential to detect and
eliminate optic disc. Several methods have been reported in the literature for optic disc detection [20.
21, 22]. From the literature survey, it is observed that the fundus images used for performing the
experiments are either from the standard dataset [23] or have been collected locally and that the
number of images used is limited. In this paper, we present an efficient method for automatic
classification of DR using morphological operations. The work presented is modification to the
method proposed in [7] in consultation with senior ophthalmologist. Further, to validate the proposed
method we perform experiments on a large dataset collected from a research center [27].
In the proposed method, we consider three features namely, blood vessels, microaneurysms
and exudates to detect diabetic retinopathy stages. The fundus image is divided into four quadrants
and the area of microaneurysms and exudates is computed in each of the four quadrants. SVM
classifier is adopted to classify the fundus image into different stages of NPDR.
2. MATERIALS
The fundus images required for performing experiment are collected from Karnataka Institute
of Diabetology, Bangalore. The experiments are performed on 337 digital color fundus photographs,
of which 321 fundus images of dimension 4288X2848, captured by NIKON D300 camera. And 16
fundus images of dimension 786X584, captures by a Canon CR5 non-mydriatic 3CCD camera are
selected from DRIVE dataset [23].
3. PROPOSED METHOD
There are three contributions in the proposed work first, pre processing the local database
images [24], second, implementation of 4-2-1 rule for classification of NPDR into different stages.
Third, deciding the structuring element for detecting blood vessels and microaneurysms that works
well for the local database and the standard DRIVE dataset [23]. Features are computed from the pre
processed fundus image in reference with ophthalmologist. Distribution of microaneurysms and
exudates on retinal surface are computed by dividing the fundus image into four quadrants to
facilitate implementation of 4-2-1 rule of NPDR classification. The following section describes
feature extraction methods.
3.1 Blood Vessel Detection
The block diagram for extracting the blood vessels is shown in figure 1. Morphological
operations are applied to the pre processed input image to extract blood vessels followed by optic
disk detection and elimination [25]. Empirically, ball shaped structuring element of size 8 is used to
detect the blood vessels. Due to elimination of optic disk, the blood vessels in it are also lost; hence
morphological reconstruction is applied to retrieve the lost blood vessels. At this stage, segmentation
is performed to eliminate other features like microaneurysms and exudates. Finally, the area of the
blood vessels is calculated. Figure 2 presents few examples of blood vessels detected. In normal
case, retina contains healthy blood vessel network (fig.2.a), hence the area occupied by the blood
vessels is more. The area of blood vessel in retina affected by mild or moderate NPDR is less as
compare to normal retinal (fig.2.b, 2.c). And it is very less in retina affected by severe NPDR
(fig.2.d).

3


Fig. 1. Block Diagram for Blood Vessel Extraction

Fig: 2. (a) Normal, (b)Mild NPDR, (c) Moderate NPDR, (d) Severe NPDR
3.2 Microaneurysms Detection
The block diagram for extracting microaneurysms is shown in figure 3. The pre-processed
image is segmented in two stages. Firstly, edge detection is achieved by using canny edge detector as
it is used to detect edges in a very robust manner [26]. Secondly, noise and other non-microaneurysm
features like exudates are eliminated by applying thresholding technique. Next, morphological
operations with disk shaped structuring element of size 6 are used to eliminate blood vessel network.
The resulting image with only microaneurysms is then divided into four quadrants and area occupied
by microaneurysms in each of the quadrants is computed. Few example images of microaneurysms
detection is shown in figure 4. Obviously, normal retinal do not contain microaneurysms (fig.4.a),
retinal affected with mild NPDR contains at least one microaneurysm but limited to few in one
quadrant (fig.4.b). Moderate NPDR retina contains few microaneurysms distributed in at least two
quadrants (fig.4.c) and in case with severe NPDR numerous microaneurysms are present in all the
four quadrants (fig.4.d).

Fig. 2. Block Diagram for Microaneurysms Extraction
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Fig. 3. (a) Normal, (b)Mild NPDR, (c) Moderate NPDR, (d) Severe NPDR
3.3 Exudates Detection
The block diagram for exudates detection is shown in figure 5. First, to facilitate exudates
detection, the optic disc is located and eliminated [23]. Thresholding and morphological operations
are then applied to detect the exudates. Few example images of exudates detection is shown in figure
6. Normal retinal image do not contain exudates (fig.6.a), retina affected with mild NPDR may or
may not contain exudates, but if present they are limited to few in only one quadrant (fig.6.b). Retina
with moderate NPDR contains few exudates distributed in at least two quadrants (fig.6.c) and in
retinal with severe NPDR numerous exudates are present in more than three quadrants (fig.6.d).

Fig. 4. Block Diagram for Exudates Extraction

Fig: 6.(a) Normal, (b)Mild NPDR, (c) Moderate NPDR, (d) Severe NPDR
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4. RESULTS
The proposed method has been evaluated using 337 fundus images collected from the
Karnataka Institute of Diabetology, Bangalore. All the three features of Diabetic retinopathy have
been detected successfully. In the normal images the blood vessels occupy the larger area and
microaneurysms and exudates are absent. In case of mild NPDR and moderate NPDR, the
microaneurysms and exudates showed their presence and in severe NPDR their prominence is more.
The SVM classifier has been used for classification. The features extracted were fed to SVM
classifier for classified the fundus images as normal, mild NPDR, moderate NPDR, and severe
NPDR. An average accuracy of 100%, 93.33%, 100% and 86.67% is obtained for normal, mild
NPDR, moderate NPDR, and severe NPDR, respectively. Sensitivity of 96.08% and specificity of
97.92% is observed. The details of the classification obtained are presented in Table1. The
recognition results in case of sever NPDR is low compared to other stages, since many of the fundus
images with severe NPDR were misclassified as moderate NPDR.
Table 1. DR recognition result using SVM classifier
Stages

No. of data sets
used for training

No. of data sets used
for testing

% of correct
classification

Normal

22

15

100

Mild NPDR

60

40

93.33

Moderate NPDR

60

40

100

Sever NPDR

60

40

86.67

5. CONCLUSION
In this paper, an automated method for detecting different stages of Non Proliferative
Diabetic Retinopathy stages is implemented using pathologies associated with DR namely, blood
vessels, exudates and microaneurysms. The proposed method is efficient in terms of number of
features used and recognition accuracy as against [7] in the literature. The classification of stages of
NPDR is based upon the presence of exudates and microaneurysms and their distribution in four
quadrants of the retinal images. The results are demonstrated for a large dataset of fundus images
that includes local dataset and DRIVE dataset. The system is able to classify the NPDR stages into
normal, mild NPDR, moderate NPDR and severe NPDR with an average accuracy of 95%, an
average sensitivity of 96.08% and an average specificity of 97.92%. As observed from the results,
we are working towards improving the feature set to increase the recognition accuracy for severe
NPDR cases.
6. ACKNOWLEDGMENT
This research is funded by UGC under UGC MRP F.40-257/2011(SR) and DST under
INSPIRE fellowship. We would like to thank Karnataka Institute of Diabetology, Bangalore,
Karnataka, for extending support by providing the fundus images to carry out the research work.
6


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