Spectral-Domain Optical Coherence Tomography (SDOCT) allows for in-vivo video-rate investigation of biomedical tissue depth structure with the purpose of non-invasive optical diagnostics. In ophthalmic applications, it has been suggested that Optical Coherence Tomography (OCT) can be used for diagnosis of glaucoma by measuring the thickness of the Retinal Nerve Fiber Layer (RNLF). We present here an automated method for determining the RNFL thickness map from a 3-D dataset. Boundary detection has been studied since the early days of computer vision and image processing, and different approaches have been proposed. The procedure described here is based on edge detection using a deformable spline (snake) algorithm. As the snake seeks to minimize its overall energy, its shape will converge on the image contour, the boundaries of the nerve fiber layer. In general, the snake is not allowed to travel too much, and therefore, proper initialization is required. The snake parameters, elasticity, rigidity, viscosity, and external force weight are set to allow the snake to follow the boundary for a large number of retinal topographies. The RNFL thickness map is combined with an integrated reflectance map of the retina and retinal cross-sectional images (OCT movie), to provide the ophthalmologist with a familiar image for interpreting the OCT data. The video-rate capabilities of our SDOCT system allow for mapping the true retinal topography since the motion artifacts are significantly reduced as compared to slower time-domain systems.