Diabetic Retinopathy Detection Using Automated Segmentation Techniques

Abstract

This paper contains a brief discussion about Diabetic Retinopathy. As the name indicates, it’s a medical complication present in diabetic patients which affects the retina. Diabetic Retinopathy acronymed as DR is a medical circumstance where the high glucose levels in the blood start affecting the blood vessels in the retina. The paper discusses the non-invasive technical method to detect diabetic retinopathy involving various algorithms in every phase of the process. The input fundus images are taken from STARE Database. The methodology conveyed in this paper involves contrast-limited adaptive histogram equalization for noise cancellation purposes and enhancing the base contrast of the image. The Segmentation consists of 2 steps and the first step consists of the Fuzzy C-Means clustering primarily to find the coarse vessels present in the retina. Additionally, the Region-based active contour is used to select the region of interest which is to highlight the blood vessels. As a result, Our proposed segmentation method extracts the blood vessels accurately, resulting in the similarity measure value of 85%. Furthermore, these segmented retinal blood vessels are given as the input to CNN classifiers in order to detect Diabetic Retinopathy. For our proposed method, an overall accuracy to detect DR was 92%. This methodology can be used for mass screening processes in the field of ophthalmology.