Abstract:
Diabetic Retinopathy (DR) is a disorder of the retinal vasculature. It happens in nearly all
patients with long-standing diabetes mellitus and can result in blindness. Screening of DR is
essential for both early detection and early treatment of the disease. The presence of exudates is
one of the complications of diabetes mellitus that is considered as the major cause of vision loss
among people around the world. It results from leakage of fluid rich in fat and cholesterol from
damaged retinal vasculature. The current trend for hard exudates (HE) detection is a visual
grading method that is time-consuming and susceptible to observer errors. The computer-aided
detection of HE would potentially assist in achieving a fast and accurate diagnosis. Even though
numerous researches are done previously to come up with a method to detect DR, further
improvement is needed for butter accuracy.
This thesis study develops an automatic method for DR detection and subsequently develops an
effective system for the segmentation of exudates. The segmented exudates indicate the presence
of DR. Before the detection of exudates, the color retinal images which were collected from
international databases IDRiD, Kaggle and e-optha Ex datasets and local images from JUMC
were classified as images with DR or healthy using local binary pattern (LBP) texture descriptor
and gray level co-occurrence matrix (GLCM) followed by segmentation of exudates using the
morphological top-hat. Different performance metrics were used to measure the performance of
the developed system. The system offered an accuracy of 98.8% for the classification of DR
positive and healthy images. It offered an accuracy of 97.8%, sensitivity of 96.3%, specificity of
90%, dice score 70%, and jaccard similarity 90.9% for segmentation of exudates. The developed
system also counts the number of exudates by counting white pixels in the image in each stage of
the DR to just indicate how the number of exudates increases with the stage. The method was
better in accuracy compared to other methods reported in the literature.
