An Improved Segmentation Technique for Early Detection of Exudates of Diabetic Retinopathy Disease

Faleh H Mahmood,Shahad Abdul-Jabbar Aziz

doi:10.24996/ijs.2019.60.11.25

Abstract

Diabetic retinopathy (DR) is a diabetes- caused disease that is associated with leakage of fluid from the blood vessels into the retina, leading to its damage. It is one of the most common diseases that can lead to weak vision and even blindness. Exudates is a clear indication of diabetic retinopathy, which is the main cause of blindness in people with diabetes. Therefore, early detection of exudates is a crucial and essential step to prevent blindness and vision loss is in the analysis of digital diabetic retinopathy systems. This paper presents an improved approach for detection of exudates in retina image using supervised-unsupervised Minimum Distance (MD) segmentation method. The suggested system includes three stages; First, after image acquisition, it is pre-processed for preparing and improving its quality. Second, the simple toward interpretation and analysis of image is segmentation as another stage. In this research, we presented a method which is used for segmentation of exudates by the adaptive (supervised-unsupervised) Minimum Distance (MD) creation segmentation algorithm with two non-overlapping clusters. The method was proposed based on its performance compared with other methods and followed by exudates extraction as a final stage. This proposed framework helps the ophthalmologists to distinguish the problem earlier, which enables them to recommend a superior medication for forestalling further retinal harm.

Highlights

Diabetes has related difficulties such as vision misfortune, heart disappointment and stroke.Patients with diabetes are bound to create eye issues, for example, waterfalls and glaucoma; the impact of the illness on the retina is the principle risk to vision
Our research focuses on the development of a PC-aided (CA) automated system for the early detection of diabetic retinopathy that depends on the reliable detection of retinal lesions in fundus images
The segmentation method using minimum distance algorithm was implemented on fundus images (FIs) of various cases defects

Summary

Introduction

Diabetes has related difficulties such as vision misfortune, heart disappointment and stroke.Patients with diabetes are bound to create eye issues, for example, waterfalls and glaucoma; the impact of the illness on the retina is the principle risk to vision. In order remove outer black region from the fundus images it's important to create a mask which is a binary image (0 or 1's) This mask, which has the size of the retinal fundus images and its value and shape differ from one image to another, is applied on the original retinal images to discard the irrelevant information; multiplying the mask image with study image produced the masked image which is the extract retinal image. This mask is used to subtract the outer black region in the image [14]. Figure-6 shows the histogram of images before and after cropping outer black region

Methods

Results

Conclusion