Automatic exudate detection with improved Naïve-bayes classifier

Nowadays diabetic retinopathy is one of the most common reasons of blindness in the world. Exudates are the primary sign of this disease so the proper detection of these lesions is an essential task in an automatic screening system. In this paper, we propose a method for exudate detection which performs with high accuracy. First, we identify possible regions containing exudates using grayscale morphology. Then, we extract more than 50 descriptors for each candidate pixel to classify them. We analyzed the information content of the descriptors and selected the most relevant ones. The selected features are used to train a boosted naïve Bayes classifier. We tested this approach on the publicly available DiaretDB color fundus image database, where the proposed detector outperformed the state-of-the-art ones regarding the FScore.