The optic disc (OD) is an important anatomical feature in retinal images, and its detection is vital for developing automated screening programs. In this paper we propose a method to automatically detect the OD in fundus images using two steps: OD vessel candidate detection and OD vessel candidate matching. The first step is achieved with multi-scale Gaussian filtering, scale production, and double thresholding to initially extract the vessels’ directional map. 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 subset of a new database consisting of 139 images from a diabetic retinopathy (DR) screening programme. The OD center was successfully detected with an accuracy of 96.4% (134/139).