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Abstract-Accurate detection of exudates in the diabetic retinal images is a challenging task. The images can have varying contrast and color characteristics. In this paper authors present the performance comparison of two feature extraction methods namely color intensity features and second order texture features based on GLCM. Authors have proposed and implemented new approach for GLCM feature calculation in which the input image is divided into number smaller blocks and GLCM features are computed on these blocks. The performance of each feature extraction method is evaluated using Back Propagation Neural Network (BPNN) classifier that is classifying the blocks as either abnormal block or normal block. With GLCM features, an accuracy of 76.6% was obtained and with color features an accuracy of 100% was obtained. It was found that color features are better in identifying true positives than GLCM based texture features. However use of GLCM features reduces the occurrence of false positives.

Automated early detection of exudates in retinal images is a challenging task. With the global diabetic population increasing at an alarming rate, there is need for development of automated systems for detection of exudates. The main obstacle in exudates detection is extreme variability of color and contrast in retinal images that depends on the degree of pigmentation, size of the pupil and illumination. The aim of this paper is to develop and validate systems for detection of hard exudates and classify the input image as normal or diseased one. The authors have proposed and implemented novel method based on color and texture features. Performance analysis of SVM and KNN classifiers is presented. Images classified by these classifiers are validated by expert opthamalagists. .

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