Greedy segmentation based diabetic retinopathy identification using curvelet transform and scale invariant features
Diabetic retinopathy (DR) is the major reason of vision loss in the active population. It can usually be prevented by regulating the blood glucose and providing a timely treatment. DR has clinical features recognized by the experts including the blood vessel area, exudates, neovascularization, hemorrhages and microaneurysm. Because DR has some varieties and complexities due to its geometrical and haemodynamic features, it is hard to detect DR in time-consuming manual diagnosis. In Computer Aided Diagnosis (CAD) systems, the fundus image features of DR are detected using computer vision techniques. In this paper, a CAD system is proposed which distinguishes automatically whether the fundus is normal or has diabetic retinopathy disease. Morphological operations like filtering, opening and dilation are applied to the fundus images for pre-process. Then, Optic Disk (OD) segmentation is implemented using Greedy algorithm. Because of the intensity of an OD is similar with some DR intensities, OD regions are eliminated in fundus images for an accurate feature extraction. The features extracted with Curvelet Transform (CT) and Scale Invariant Feature Transform (SIFT) respectively are concatenated to provide a feature set that defines the fundus data optimally. Then, the feature set is given to Support Vector Machines (SVM) method for classification purposes. The proposed method has an accuracy of 92.8%, a sensitivity of 0.988% and a specificity of 0.80%.