Application of intuitionistic fuzzy histon segmentation for the automated detection of optic disc in digital fundus images.

Human eye is the most sophisticated organ, with perfectly interrelated subsystems such as retina, pupil, iris cornea, lens and optic nerve. Uncontrolled diabetes retinopathy (DR) and glaucoma may lead to blindness. Optic disc helps to identify the different stages of DR, and glaucoma. In this paper, a novel automated, reliable and efficient optic disc localization and segmentation method using digital fundus images is proposed. General-purpose edge detection algorithms often fail to segment the optic disc (OD) due to fuzzy boundaries, inconsistent image contrast or missing edge features. This paper proposes a novel and probably the first method, using Attanassov intuitionistic fuzzy histon (A-IFSH) based segmentation to segment optic disc in retinal fundus images. Optic disc pixel intensity and column wise neighbourhood operation is employed to locate and isolate the optic disc. The method has been evaluated on 100 images comprising 30 normal, 39 glaucomatous and 31 DR images. Our proposed method yielded precision-0.93, recall-0.91, F-score-0.92 and mean segmentation accuracy of 93.4%. We have also compared the performance of our proposed method with Otsu and Gradient vector flow (GVF) snake methods. Overall, our result shows the superiority of proposed fuzzy segmentation technique over other two segmentation methods.