  Sampling from distributions of implicitly defined shapes enables analysis of
various energy functionals used for image segmentation. Recent work describes a
computationally efficient Metropolis-Hastings method for accomplishing this
task. Here, we extend that framework so that samples are accepted at every
iteration of the sampler, achieving an order of magnitude speed up in
convergence. Additionally, we show how to incorporate topological constraints.
