Graph based detection of optic disc and fovea in retinal images

Diabetic retinopathy (DR) is the damage to the eye's retina that occurs with long-term diabetes, which can eventually lead to blindness. Screening programs for DR are being introduced, however, an important prerequisite for automation is the accurate localization of the main anatomical features in the image, notably the optic disc (OD) and the macula. A series of interesting algorithms have been proposed in the recent past and the performance is generally good, but each method has situations, where it fails. This paper presents a novel framework for automatic detection of optic disc and macula in retinal fundus images using a combination of different optic disc and macula detectors represented by a weighted complete graph. A node pruning procedure removes the worst vertices of the graph by satisfying predefined geometric constraints and get best possible detector outputs to be combined using a weighted average. The extensive tests have shown that combining the predictions of multiple detectors is more accurate than any of the individual detectors making up the ensemble.