Exudates detection in fundus images using mean-shift segmentation and adaptive thresholding

Abstract

Diabetic retinopathy (DR) affects changes to retinal blood vessels that can cause them to bleed or leak fluid and distorting vision. An early detection of exudates is a prerequisite for detecting and grading severe retinal lesions, like DR. This paper presents an automated method for detection of the exudates in digital fundus images. Our approach can be divided into four steps: shifting colour correction, Optic disc (OD) elimination, exudates segmentation and separation of exudates from background. In order to correct non-uniform illumination, we adopted the grey world method. Then, we must extract the OD prior to the process because it appears with similar colour, intensity and contrast to exudates. Next, to segment the exudates, we applied the mean-shift method. Finally, we used the maximum entropy thresholding to separate the exudates from background. The proposed method is tested on DIARETDB0 and DIARETDB1. Comparing to other recent methods available in the literature, our proposed approach obtains better exudate detection results in terms of sensitivity, specificity and accuracy.