Optical Coherence Tomography (OCT) is a non-invasive, depth-resolved imaging modality that has become a prominent ophthalmic diagnostic technique. We present a novel segmentation algorithm based on Chan-Vese's energy-minimizing active contours to detect intra-retinal layers in OCT images. A multi-phase framework with a circular shape prior is adopted to model the boundaries of retinal layers and estimate shape parameters using least squares. We use a contextual scheme to balance the weight of different terms in the energy functional. The results from various synthetic experiments and segmentation results on rat OCT images are presented, demonstrating the strength of our method to detect the desired layers with sufficient accuracy even in the presence of intensity inhomogeneity. Our algorithm achieved an average Dice similarity coefficient of 0.84 over all segmented layers, and of 0.94 for the combined nerve fiber layer, ganglion cell layer, and inner plexiform layer, which are critical layers for glaucomatous degeneration.