Diagnosis of diabetic retinopathy based on holistic texture and local retinal features

In this paper, eye fundus images are analyzed for the automatic detection of diabetic retinopathy. One thousand two hundred eye fundus images of the Messidor database were used to test the system using the cross validation in various settings. Two types of features were extracted including the holistic texture features and the local retinal features. Four classifiers were implemented including the k-nearest neighbors, neural networks, support vector machines, and random decision forests. The best results from the analysis of holistic texture features were obtained for the Independent Component Analysis method, which had never been tested before in this type of image. Furthermore, the performance of our system improved greatly when two local retinal features - micro-aneurysms and exudates - were incorporated into the analysis, a methodology inspired by a modular approach originally developed for face-recognition tasks. The diagnostic performance of our algorithm is very promising and similar to previous automatic systems and human expert analysis on the same dataset. This framework has the potential to be used as an aiding tool for the diagnosis of diabetic retinopathy.