Detection and classification of exudates by extracting the area from RGB fundus images

Abstract

A technique for exudate detection in fundus image is been presented in this paper. Due to diabetic retinopathy, an abnormality is caused known as exudates. The loss of vision can be prevented by detecting the exudates as early as possible. The work mainly aims at detecting exudates which have been present in the green channel of the RGB image by applying few preprocessing steps, 2D-DWT and feature extraction. The extracted features are fed to three different classifiers such as KNN, SVM, and NN. Based on the classifiers result the exudate is classified as normal, soft exudate and hard exudate, if exudate is present the extraction of ROI of exudate is done based on canny edge detection followed by morphological operations. The severity of the exudates is established in the area of the detected exudate. The NN, with ROI, was smeared on RGB fundus images for location of exudate. The NN was castoff with image processing methods by which we achieved a 100% success rate.