Markov chain Monte Carlo method for tracking myocardial borders

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 contour to capture the endo- and epicardial borders (if the left ventricle in a mouse heart. The contour is initialized by computing the ellipse 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 contour. The GICOV is maximal when the contour lies along strong, relatively constant gradients. The contour 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.