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Optic Disc Detection Using Vessel Characteristics and Disc Features

For the screening of any eye disease, the detection of Optic disc is a crucial step. In this paper, a novel and robust technique is proposed to detect the Optic nerve head (ONH) in the fundus image automatically. The proposed algorithm uses the directional characteristics of vessel structure and a set of characteristics of optic nerve head as the key factors for the detection. The ideas behind the robustness of this approach are the key features which consider all the properties of optic disc, such as Image Variance, Density of Vessel pixels, number of connected components, Vessel intersection points, maximum intensity regions and angle of intersection between vessel segments. A well-defined combination of all these features, with the inclusion of parabola fitting approach to the extracted vessel structure, helps us to identify a mask which contains 40%–100% portion of the Optic disc. Our proposed method has been evaluated using six public databases containing both normal and diseased retinas. The Optic disk mask was successfully located in 1642 images out of 1650 images (99.77%). Average computation time is 3.44–4 sec.