Maximum entropy principle for exudates segmentation in retinal fundus images

Abstract

This paper addresses a novel segmentation algorithm for detecting one of the diabetic retinopathy pathologies, called “exudates”. Exudates segmentation is ordinarily examined from retinal fundus images by various image processing techniques. Instead of carefully picking up the specific exudates features on the retinal images as has been done by the other works, our scheme is to observe global information of the retinal images. The global information, as well as spatial information, is extracted by maximum entropy-based thresholding. The proposed algorithm determines a reasonable threshold value for separating exudates areas, which are usually sparse and brighter, from the rest of images. This approach also ensures and minimizes the illumination variance effects of different images since it takes into account the global information. In addition to the proposed algorithm, luminance channel of the retinal images is exploited for pre-processing stage. After the optical disc which has similar characteristic to the exudates is separated, the pathological areas are subsequently acquired. Evaluations on the E-OPHTHA-EX retinal fundus images database show the advantages of the proposed approach, with the accuracy 99.4 percent, specificity 99.6 percent, and sensitivity 16.9 percent.