A new curvelet transform based method for extraction of red lesions in digital color retinal images

Red lesions in the form of Microaneurysms (MAs) and Hemorrhages (HMs) are among the first explicit signs of diabetic retinopathy (DR). Hence robust detection of these lesions is an important diagnostic task in computer assistance systems. In this paper we present a new curvelet based algorithm to separate these red lesions from the rest of the color retinal image. In order to prevent fovea to be considered as red lesion, we introduce a new illumination equalization algorithm and apply that to green plane of retinal image. In the next stage, we apply digital curvelet transform (DCUT) to produced enhanced image and modify curvelet coefficients in order to lead red objects to zero. Then we separate these lesions as candidate region by applying an appropriate threshold. Finally, the total structure of blood vessel is extracted employing a curvelet-based technique and the false positives (FPs) are eliminated by subtracting the vessel structure from the candidate images. Experiments on 89 retinal images of diabetic patients indicate that we are able to achieve 94% sensitivity and 87% specificity in detection of red lesion.