Automated detection of exudates in retinal images using a split-and-merge algorithm

Retinal image analysis is commonly used for the diagnosis and monitoring of diseases. In fundus photographs, bright lesions representing hard and soft exudates are the earliest signs of diabetic retinopathy. In this paper, an automated method for the detection of these exudates in retinal images is presented. Candidates are detected using a combination of coarse and fine segmentation. The coarse segmentation is based on a local variation operation to outline the boundaries of all candidates which have clear borders. The fine segmentation is based on an adaptive thresholding and a new split-and-merge technique to segment all bright candidates locally. Using a clinician's reference for ground truth exudates were detected from a database with 89.7% sensitivity, 99.3% specificity and 99.4% accuracy. Due to its distinctive performance measures, the proposed method may be successfully applied to images of variable quality.