This paper addresses the regional boundary extraction problem, which attempts to match a reference template contour to a desired target boundary in the presence of noise. A three-step iterative strategy is used: first, a local correspondence is set up between each pixel of the reference template and the target boundary; second, shape information is used to minimize the error from the first step; and third, the template contour is updated. The first step assigns a target pixel to each pixel of the template contour, using an iterated max-min estimator which gives the upper bound for the mean square error. The second step minimizes error by using correlation information between the template pixels; the correlation is approximated as a Gaussian weighted distance function, and effectively smoothes the template contour deformation from the first step. The third step uses this smoothed deformation to update the template contour, which then becomes the starting point for the next iteration. Experimental results are shown in which the algorithm is applied to medical NMR images and performance is compared to the SNAKE algorithm.