Detection of exudates in retinal images using a pure splitting technique

Diabetic retinopathy is a major cause of blindness. Earliest signs of diabetic retinopathy are damage to blood vessels in the eye and then the formation of lesions in the retina. This paper presents an automated method for the detection of bright lesions (exudates) in retinal images. In this work, an adaptive thresholding based on a novel algorithm for pure splitting of the image is proposed. A coarse segmentation based on the calculation of a local variation for all image pixels is used to outline the boundaries of all candidates which have clear borders. A morphological operation is used to refine the adaptive thresholding results based on the coarse segmentation results. Using a clinician reference standard (ground truth), images with exudates were detected with 91.2% sensitivity, 99.3% specificity, and 99.5% accuracy. Due to its results the proposed method can achieve superior performance compared to existing techniques and is robust to image quality variability.