Detection and Classification of Non-Proliferative Diabetic Retinopathy Stages Using Morphological Operations and SVM Classifier

1. Detection and Classification of Non-
Proliferative Diabetic Retinopathy Stages
Using Morphological Operations and SVM
Classifier
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Mohammed Shafeeq Ahmed
Research Scholar,
Dept. of Computer Science,
Research and Development Center, Bharathiar University, Coimbatore

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Overview
1. Introduction
1. What is Diabetic Retinopathy
2. Classification of Diabetic Retinopathy
2. Material Collected
3. Methodology
4. Experimental Results
5. Conclusion
6. References

3. • Peoples with diabetes can have an eye disease called diabetic retinopathy.
• This is when high blood sugar levels cause damage to blood vessels in the
retina. These blood vessels can swell and leak. Or they can close, stopping
blood from passing through. Sometimes abnormal new blood vessels grow
on the retina. All of these changes can steal your vision.
• Diabetic retinopathy is a term used for all the abnormalities of the small
blood vessels of the retina caused by diabetes, such as weakening of blood
vessel walls or leakage from blood vessels.
• Diabetic retinopathy is a complication of diabetes that results from damage
to the blood vessels of the light-sensitive tissue at the back of the retina.
• Diabetic retinopathy affect anyone who has type 1 diabetes or type 2
diabetes. The longer a patient has diabetes, the more likely they are to
develop diabetic retinopathy.
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INTRODUCTION
What is Diabetic Retinopathy?

4. • There are two main stages of diabetic eye disease.
• Non-proliferative retinopathy is a common, usually mild form that generally
does not interfere with vision. Abnormalities are limited to the retina and
usually will only interfere with vision if it involves the macula. If left untreated
it can progress to proliferative retinopathy
• Proliferative retinopathy, the more serious form, occurs when new blood
vessels branch out or proliferate in and around the retina. It can cause bleeding
into the fluid-filled center of the eye or swelling of the retina, and lead to
blindness.
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Diabetic Retinopathy
A normal retina
A retina showing a sign
of diabetic retinopathy

5. This is the early stage of diabetic eye disease. Many people with diabetes have
it.
Stage 1 : Mild Non-proliferative Retinopathy.
At this earliest stage, microaneurysms occur. They are small areas of balloon-
like swelling in the retina's tiny blood vessels.
Stage 2 : Moderate Non-proliferative Retinopathy.
As the disease progresses, some blood vessels that nourish the retina are
blocked.
Stage 3 : Severe Non-proliferative Retinopathy
Many more blood vessels are blocked, depriving several areas of the retina
with their blood supply. These areas of the retina send signals to the body to
grow new blood vessels for nourishment.
Stage 4 : Proliferative Retinopathy.
At this advanced stage, the signals sent by the retina for nourishment trigger
the growth of new blood vessels which are abnormal and fragile. They grow
along the retina and along the surface of the clear, vitreous gel that fills the
inside of the eye. By themselves, these blood vessels do not cause symptoms
or vision loss. However, they have thin, fragile walls. If they leak blood,
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Classification of Non-Proliferative
Diabetic Retinopathy (NPDR)

6. The implementation of the detection method proposed was performed in
MATLAB. The accuracy of the method was tested in public database of fundus
images DIARETDB1. The DIARETDB1 has a total of 89 (RGB) fundus
images of size 1500 x 1152. Out of this total, 84 images have characteristic
signs of DR, such as microaneurysms, hemorrhages and exudates, and 5
images are of normal retinas.
Further we have conducted the experiment on different dimension like (4288 x
2848), (786 x 584), captured from different camera and DRIVE dataset. To
check the performance of the proposed algorithm.
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Material Collection

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Methodology
Feature Extraction
SVM Classifier
Pre-processing
Applying Morphological Method
Input Fundus Images
Mild Moderate Severe
Work Flow of NPDR Classification
•We proposed an efficient method for
automatic detection and classification of
NPDR stages using morphological
operations and SVM classifier.
•We perform experiments on a large
dataset collected from the publicly
available DIARETDB1 and DRIVE
database.
•In the proposed method, blood vessels,
MAs and exudates are used to detect DR
stages. The technique used is by dividing
the fundus image into four quadrants and
the area of MAs and exudates is computed
at each of the four quadrants. SVM
classifier is used to identify the different
stages of NPDR in fundus image.

8. Preprocessing:
•Input images is converted to a standard size to improve the quality of input
image.
•Green channel is used for feature extraction of DR from fundus images.
•Contrast enhancement of the output image is done using CLAHE approach.
Blood Vessel Detection:
•Morphological operation is used for extraction of blood vessels and detection
and elimination of Optic Disc.
•Segmentation is used to eliminate other features like MAs and exudates.
•Last the area of blood vessels is calculated.
•Blood vessels area is more in affected retina as compare to the normal retina.
•Hence we classifies as mild, moderate and severe NPDR.
Microaneurysms Detection:
•Edge detection is achieved by using Canny Edge detector.
•Thresholding technique is used to eliminate noise and exudates.
•The resultant image is divided into four quadrants and compute the area
occupied by MAs in each quadrants.
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9. Exudates Detection:
•Exudates are detected using Morphological operations.
•Normal retinal image does not contain any exudates.
•Mild NPDR may contain exudates, but only in one quadrant.
•Moderate NPDR are affected with exudates and distributed in at least two
quadrants.
•Severe NPDR are affected with numerous exudates and present in almost all
the quadrants.
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10. • The proposed method has been evaluated using 129 fundus images
collected from the DIARETDB1 and DRIVE database.
• All the three features of DR have been detected successfully.
• In the normal images the blood vessels occupy the larger area and MAs and
exudates are absent.
• In the case of mild NPDR and moderate NPDR, the MAs and exudates
showed their presence and in severe NPDR their prominence is more.
• The SVM classifier has been used for classification.
• The features were classified 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. The
sensitivity of 96.08% and specificity of 97.92% is observed. The details of
the classification obtained are presented in Table1.
• Drawback: The recognition results in the case of sever NPDR is low
compared to other stages since many of the fundus images with severe
NPDR were misclassified as moderate NPDR.
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Experimental Results

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No MAs or Exudates Detected
(NORMAL) Fundus Images

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Mild NPDR Detected Fundus Images

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Moderate NPDR Detected Fundus
Images

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Sever NPDR Detected Fundus
Images

15. • An automated system was developed to detect and classify the NPDR
stages from the fundus images using SVM classifier and achieved a high
percentage of sensitivity and specificity.
• The adopted methodology is more efficient and effective than other
methods.
• We have used 129 fundus images collected from the DIARETDB1 and
DRIVE databases.
• The performance of the method for detection and classification of NPDR
stages from fundus images has achieved a high successful percentage.
• This method is highly flexible to other databases.
• The system has classified the NPDR stages in 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%.
• The main purpose of this proposed work is to help the ophthalmologists in
screening the DR using the SVM classifier.
• Thus, this SVM technique has given a successful DR screening method
which helps to detect the disease in early stage and prevents vision loss.
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Conclusions

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morphological techniques”, International Conference on Recent Trends in Engineering and Material Sciences, 2016.
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Reference

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Thank You

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