Automatic detection of micro-aneurysms in retinal images based on curvelet transform and morphological operations

Abstract

Diabetic retinopathy (DR) is one of the major complications of diabetes that changes the blood vessels of the retina and distorts patient vision that finally in high stages can lead to blindness. Micro-aneurysms (MAs) are one of the first pathologies associated with DR. The number and the location of MAs are very important in grading of DR. Early diagnosis of micro-aneurysms (MAs) can reduce the incidence of blindness. As MAs are tiny area of blood protruding from vessels in the retina and their size is about 25 to 100 microns, automatic detection of these tiny lesions is still challenging. MAs occurring in the macula can lead to visual loss. Also the position of a lesion such as MAs relative to the macula is a useful feature for analysis and classification of different stages of DR. Because MAs are more distinguishable in fundus fluorescin angiography (FFA) compared to color fundus images, we introduce a new method based on curvelet transform and morphological operations for MAs detection in FFA images. As vessels and MAs are the bright parts of FFA image, firstly extracted vessels by curvelet transform are removed from image. Then morphological operations are applied on resulted image for detecting MAs.