Automatic exudate detection using active contour model and regionwise classification

Diabetic retinopathy is one the most common cause of blindness in the world. Exudates are among the early signs of this disease, so its proper detection is a very important task to prevent consequent effects. In this paper, we propose a novel approach for exudate detection. First, we identify possible regions containing exudates using grayscale morphology. Then, we apply an active contour based method to minimize the Chan-Vese energy to extract accurate borders of the candidates. To remove those false candidates that have sufficient strong borders to pass the active contour method we use a regionwise classifier. Hence, we extract several shape features for each candidate and let a boosted Naïve Bayes classifier eliminate the false candidates. We considered the publicly available DiaretDB1 color fundus image set for testing, where the proposed method outperformed several state-of-the-art exudate detectors.