Automated identification of diabetic retinal exudates in digital colour images.

Abstract

To identify retinal exudates automatically from colour retinal images. The colour retinal images were segmented using fuzzy C-means clustering following some key preprocessing steps. To classify the segmented regions into exudates and non-exudates, an artificial neural network classifier was investigated. The proposed system can achieve a diagnostic accuracy with 95.0% sensitivity and 88.9% specificity for the identification of images containing any evidence of retinopathy, where the trade off between sensitivity and specificity was appropriately balanced for this particular problem. Furthermore, it demonstrates 93.0% sensitivity and 94.1% specificity in terms of exudate based classification. This study indicates that automated evaluation of digital retinal images could be used to screen for exudative diabetic retinopathy.