Retinal Image Analysis for Abnormality Detection-An Overview

A. A.

doi:10.3844/jcssp.2012.436.442

Abstract

Problem statement: Classification plays a major role in retinal image analysis for detecting the various abnormalities in retinal images. Classification refers to one of the mining concepts using supervised or unsupervised learning techniques. Approach: Diabetic retinopathy is one of the common complications of diabetes. Unfortunately, in many cases, the patient is not aware any symptoms until it is too late for effective treatment. Diabetic retinopathy is the leading cause of blindness. Diabetic retinopathy results in retinal disorders that include microaneursyms, drusens, hard exudates and intra-retinal micro-vascular abnormalities. Results: An automatic method to detect various lesions associated with diabetic retinopathy facilitate the opthalmologists in accurate diagnosis and treatment planning. Abnormal retinal images fall into four different classes namely Non-Proliferative Diabetic Retinopathy (NPDR), Central Retinal Vein Occlusion (CRVO), Choroidal Neo-Vascularization Membrane (CNVM) and Central Serous Retinopathy (CSR). Conclusion: In this study, we have analyzed the various methodologies for detecting the abnormalities in retinal images automatically along with their merits and demerits and proposed the new framework for detection of abnormalities using Cellular Neural Network (CNN).

Highlights

Diabetic Retinopathy (DR) is a sight threatening complication due to diabetes mellitus that affects the retina
According to the Malaysia National Eye Database 2007, among 10,856 cases with diabetes, 36.8% has any form of DR, of which 7.1% comprises Proliferative Diabetic Retinopathy (PDR) (Hani et al, 2010)
DR is usually asymptomatic in its beginning, so diabetic patients do not undergo any eye examination until it is already too late for an optimal treatment and severe retinal damages have been caused

Summary

INTRODUCTION

Diabetic Retinopathy (DR) is a sight threatening complication due to diabetes mellitus that affects the retina. Regular retinal examinations for diabetic patients guarantee an early detection of DR reducing significantly the incidence of blindness cases. The problem of diagnosis lies in the huge amount examinations which has to be performed by the Fig. 1: Microaneursym in retinal image specialists to detect the abnormalities. To ensure that treatment is received on time, the eye fundus of diabetic patients needs to be examined at least once a year. The earlier clinical studies use the standardized, validated Wisconsin grading system of retinopathy which is performed by an experienced ophthalmologist or grader using standard photographs This method is a time-consuming process which requires significant training and exercise and is vulnerable to observer error (Ngyen et al, 1996). Caused by diabetic retinopathy such as microaneurysm, hard exudate, soft exudate, hemorrhage and neo-

MATERIALS AND METHODS

RESULTS

DISCUSSION

CONCLUSION