The retinal optic disc is the region from where the central retinal artery and optical nerve of the retina emanate. Hence, it often serves as an important landmark and reference for other features in a retinal fundus image. The features obtained from a fundus images are often helpful in the diagnosis of various eye diseases. Locating and segmenting the optic disc are key pre-processing steps for extracting retinal features. This paper proposes a statistics-based method for locating a rectangular region of interest (ROI) containing the optic disc in a retinal fundus image. From a set of candidate rectangular regions, the method chooses the ROI using statistical features, namely the mean, standard deviation, and skewness of the pixel gray levels in the candidate regions. Since an optic disc is approximately round or slightly oval in the vertical direction, this study treats the maximal inscribed circle of the ROI as the initial contour of the optic disc and uses an active contour model (ACM) to precisely segment the optic disc further based on the initial contour. The experimental results show that the proposed statistics-based method combined with an ACM provides impressive performance in the segmentation of optic discs.