A Gaussian mixture model based system for detection of macula in fundus images

Abstract

Digital fundus imaging is used to diagnose various eye diseases like diabetic retinopathy, diabetic maculopathy and age related macular degeneration. Macula is the main central part of retina which is responsible for sharp vision and any changes in macula cause severe effects on vision. In this paper, we propose a novel method for automated detection of macula from digital fundus images. The proposed system performs preprocessing, optic disc detection and blood vessel segmentation prior to macula detection. In macula detection, it formulates a feature vector and uses Gaussian Mixture Model for detection of macular region. We evaluate the proposed technique using publicly available fundus image database MESSIDOR. The results show the validity of proposed system and are found to be competitive with previous results in the literature. © 2012 Springer-Verlag.