This document provides an overview of diabetic retinopathy diagnosis through the analysis of retinal images. It discusses the aims of identifying patients with different stages of diabetic retinopathy. The stages of diabetic retinopathy and associated symptoms are defined. Pre-processing steps like color conversion, filtering and segmentation are described. A proposed methodology includes blood vessel and lesion detection through morphological operations, texture analysis, feature extraction and classification. Results of optic disc detection, blood vessel segmentation and texture analysis are shown. The conclusion discusses developing more accurate detection techniques and extracting smaller blood vessels to aid in diagnosis.

1. CONTENTS
•INTRODUCTION
•AIMS & OBJECTIVE
•DEFINITION OF DIABETIC RETINOPATHY
•STAGES OF DR
•SYMPTOMS OF DR
•ANALYSIS OF RETINA
•ABNORMILITIES ASSOCIATED WITH EYE
•RELATED WORK
•MORPHOLOGICAL OPERATORS
•DIABETIC RETINOPATHY DIAGNOSIS
•PRE-PROCESSING STEPS
•PROPOSED METHODOLOGY
•RESULTS
•CONCLUSION
•REFERENCES

2. Introduction
• The Devastation
– Diabetic retinopathy – 4.1 million US Adults
• National Health Interview Survey and US Census Population
– Glaucoma – 2 million individuals in the US.
• Ophthalmologic images
– Important structures – Blood Vessels
– Help detect and treat Eye Diseases affecting blood
vessels
• Retina vessel map segmentation is very important to
medical applications, such as diabetic retinopathy,
aging related retina analysis etc.

3. Introduction
• Damaged blood vessels indicate retinal
disease.
Blood clots indicate diabetic retinopathy.
Narrow blood vessels indicate Central Retinal
Artery Occlusion.
• Observation of blood vessels in retinal
images
– Shows presence of disease
– Helps prevent vision loss by early detection

4. Aims and Objective
• The primary aim of this project is to develop a system
that will be able to identify patients with BDR and
PDR from either colour image or grey level image
obtained from the retina of the patient.
• The secondary aim includes developing a MATLAB
based Graphic User Interface (GUI) tool to be used
by the ophthalmologist in marking fundus images.

5. Definition of Diabetic Retinopathy
• Diabetic retinopathy is a complication of diabetes and a leading
cause of blindness.
• It occurs when diabetes damages the tiny blood vessels inside the
retina, the light-sensitive tissue at the back of the eye.
Healthy Retina Diabetic retinopathy

6. Stages of Diabetic Retinopathy
1. Mild Nonproliferative Retinopathy
2. Moderate Nonproliferative Retinopathy
3. Severe Nonproliferative Retinopathy
4. Proliferative Retinopathy

7. Stages of Diabetic Retinopathy
(a) normal (b) mild DR (c) moderate DR
(d) severe DR (e)prolific DR

8. Risk & Cause Vision Loss of DR
• All people with diabetes—both type 1 and type 2—are at risk. That's why
everyone with diabetes should get a comprehensive dilated eye exam at
least once a year.
• Blood vessels damaged from diabetic retinopathy can cause vision loss in
two ways:
1. proliferative retinopathy
2. macular edema.
Normal vision Same scene viewed by a person with DR

9. Diabetic Retinopathy Symptoms
• Blurred vision
• Floaters
• Fluctuating vision
• Distorted vision
• Dark areas in the vision
• Poor night vision
• Impaired color vision
• Partial or total loss of vision

10. Retinal Diagnostic Tests
•Fundus Photography
•Fluorescein Angiography (FA)
•Optical Coherence Tomography (OCT)
•Ocular Ultrasonography
•Electroretinography (ERG)

11. Structure of Eye

12. Analysis of Retina

13. Abnormalities Associated with the eye
Microaneurysms Haemorrhages

14. Neovascularisation Soft &Hard exudates

15. Related Work
• Akara et. al discussed about the set of optimally
adjusted morphological operators used for exudates
detection for thai patients.
• Selvathi et. al uses fundus images are segmented &
they are classified as normal or DR images by
extracting features from these structures & GLCM

16. Morphological Operations
1. Dilation
2. Erosion
3. Opening and Closing
4. Skeletonization

17. Diabetic Retinopathy Diagnosis

18. Block diagram of Pre-Processing

19. Colour Space Conversion
Original image Gray scale image

20. Zero Padding
Input Image before zero Padding Output zero Padded Image

21. Median Filtering
Original Image before Median Filtering Image after Median Filtering

22. Histogram Equalisation
Original Image Image after Histogram Equalisation

23. Histogram of Original Image Histogram of Image after
Histogram Equalisation

24. Pre-processed Image
Original gray Image Pre- Processed Image

25. Segmentation
Segmented Image

26. Proposed Methodology

27. 5 methods for Proposed Methodology
• DATABASES: Publicly avalilable databases such as DRIVE, DIARETDB1
and MESSIDOR are used in this work.
• SEGMENTATION OF RETINAL STRUCTURE:
Detection of Blood Vessels: It is used for enhancing the retinal image.
Detection of Exudates and Microaneurysms: Morphological operators
are used for the detection of exudates and MA in this work.
• TEXTURE ANALYSIS:It gives information about the arrangement of
surface pixels and their relationship with the surrounding pixels
• FEATURE EXTRACTION:The feature vector used for classification
consists of seven features obtained from segmentation of retinal structures and
texture analysis.
• CLASSIFICATION

28. RESULTS AND DISCUSSION
OPTIC DISK DETECTION
Optic Disc Location (a) RGB Image, (b) I band after Pre-processing, (c) Image after
Closing, (d) Thresholded Image, (e)Marker Image, (f) Reconstructed Image, (g)
Threshold on Difference Image, (h) Optic Disc Area.

29. BLOOD VESSEL DETECTION
(a) Original image (b) Green channel image (c) Enhanced image
(d) Segmented image (e) Ground truth image

30. RETINAL IMAGES ENTROPY CONTRAST HOMOGENITY
6.652 [0.3241 0.3396] [0.8751 0.8754]
6.8912 [0.1504 0.1527] [0.9308 0.9344]
6.1124 [0.1268 0.1470] [0.9377 0.9324]
TEXTURE ANALYSIS RESULTS

31. RETINAL IMAGES ENTROPY CONTRAST HOMOGENITY
6.5160 [0.5008 0.4678] [0.8519 0.8600]
7.228 [0.3114 0.2509] [0.8853 0.8988]
6.409 [0.4226 0.5190] [0.8487 0.8466]

32. Conclusion and Future work
• Development of a system that will be able
to identify patients with BDR and PDR from
either color image or grey level fundus
image
• Develop techniques for not only better
detection of vessel edges,
• More anatomically exacting regarding
medical image standards.
• Extraction of Minute blood vessels.

33. REFERENCES
• DRIVE Database: http://www.isi.uu.nl/Research/Database/DRIVE
• Selvathi D, Neethi Balagopal (2012) Detection of Retinal Blood
Vessels using Curvelet Transform. IEEE International Conference on
devices, Circuits and Systems 325-329. doi:
10.1109/ICDCSyst.2012.6188730
• Pierre Soille (2002) Morphological Image Analysis, Principles and
Applications. 2nd Edition. Springer
• Bevilacqua,V., Cambò,S., Cariello,L., Mastronardi, G., ‘A combined
method to detect Retinal Fundus Features’, Conference on EACDA,
Italy, September, 2005.
• Martin M. and Tosunoglu S., 2000, “Image Processing Techniques
for Machine Vision”,Conference on Recent Advances in Robotics,
Florida, pp. 1-9.

34. THANK YOU

35. ANY QUESTIONS