Detection of Exudates Through Local Binary Pattern in Diabetic Retinopathy

Abstract

The long term diabetes leads to the retinal vascular disease called diabetic retinopathy (DR). As DR is a progressive disease, it should be diagnosed and treated as soon as possible to prevent the patient from blindness. The lesions like microaneurysms, exudates, hemorrhages and abnormal growth of blood vessels etc. will appear in DR. In this paper, the segmentation and classification of exudates is proposed. Exudates are segmented using modified morphological operation which deals with the intervention of optic disc before the detection of exudates. The feature extraction is done using local binary pattern (LBP) and classified using support vector machine (SVM). The proposed method is tested on DIARETDB0 and DIARETDB1, which are freely available datasets. The method is also evaluated on the hospital images. The average sensitivity, specificity and accuracy of 0.92, 0.82, and 0.95 respectively are obtained.