Comparative pixel-level exudate recognition in colour retinal images

Abstract

Retinal exudates are typically manifested as spatially random yellow/white patches of varying sizes and shapes. They are a visible sign of retinal diseases such as diabetic retinopathy. Following some key preprocessing steps, colour retinal image pixels are classified to exudate and non-exudate classes. K nearest neighbour, Gaussian quadratic and Gaussian mixture model classifiers are investigated within the pixel-level exudate recognition framework. A Gaussian mixture model-based classifier demonstrated the best classification performance with 89.2% sensitivity and 81.0% predictivity in terms of pixel-level accuracy and 92.5% sensitivity and 81.4% specificity in terms of image-based accuracy. © Springer-Verlag Berlin Heidelberg 2005.