The optic disc (OD) is an important anatomical feature in retinal images, and its detection is vital for developing automated screening programs. Currently, there is no algorithm designed to automatically detect the OD in fundus images captured from Asians which are larger and have thicker vessels compared to Caucasians. In this paper, we propose such a method to complement current algorithms using two steps: OD vessel candidate detection and OD vessel candidate matching. The first step is achieved with multiscale Gaussian filtering, scale production, and double thresholding to initially extract the vessels' directional map of various thicknesses. The map is then thinned before another threshold is applied to remove pixels with low intensities. This result forms the OD vessel candidates. In the second step, a Vessels' Directional Matched Filter (VDMF) of various dimensions is applied to the candidates to be matched, and the pixel with the smallest difference designated the OD center. We tested the proposed method on a new database consisting of 402 images from a diabetic retinopathy (DR) screening programme consisting of Asians. The OD center was successfully detected with an accuracy of 99.25% (399/402). 1. Introduction The optic disc is a vertical oval with average dimensions of 1.76？mm (horizontally) 1.92？mm (vertically), and situated 3-4？mm to the nasal side of the fovea [1]. In fundus imaging the OD is usually brighter than its surrounding area and is the convergence of the retinal blood vessel network. Detection of the OD is useful in the diagnosis of glaucoma, optic neuropathies, optic neuritis, anterior ischemic optic neuropathy or papilledema, and optic disc drusen. It can also be used as a marker to help locate fovea/macula [2–4], as well as decide if the image is of the left or right eye. For diabetic retinopathy, detection of the OD assists physicians identifying neovascularization of the disc (NVD) in the advanced stage of DR, proliferative diabetic retinopathy (PDR). This makes the task of automatic OD detection both relevant and necessary. A distinction needs to be made between automatic OD detection and automatic OD boundary detection. The former refers to the location of the disc center, while the latter aims to segment the OD by detecting the boundary between the retina and the nerve head. Our work is detecting the OD center. In the literature, OD detection can be categorized into various groups. The first group uses properties of the OD [5–8] such as high pixel intensity and its oval shape. Morphology [9] is also used where the OD
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