Cardiac magnetic resonance studies have led to a greater understanding of the pathophysiology of ischemic heart disease. Manual segmentation of myocardial borders, a major task in the data analysis of these studies, is a tedious and time consuming process subject to observer bias. Automated segmentation reduces the time needed to process studies and removes observer bias. We propose an automated segmentation algorithm that uses an active surface to capture the endo- and epicardial borders of the left ventricle in a mouse heart. The surface is initialized as an ellipsoid corresponding to the maximal gradient inverse of variation (GICOV) value. The GICOV is the mean divided by the normalized standard deviation of the image intensity gradient in the outward normal direction along the surface. The GICOV is maximal when the surface lies along strong, constant gradients. The surface is then evolved until it maximizes the GICOV value subject to shape constraints. The problem is formulated in a Bayesian framework and is implemented using a Markov Chain Monte Carlo technique.