Parts-based segmentation with overlapping part models using Markov chain Monte Carlo

A probabilistic method is proposed for segmenting multiple objects that overlap or are in close proximity to one another. A likelihood function is formulated that explicitly models overlapping object appearance. Priors on global appearance and geometry (including shape) are learned from example images. Markov chain Monte Carlo methods are used to obtain samples from a posterior distribution over model parameters from which expectations can be estimated. The method is described in detail for the problem of segmenting femur and tibia in X-ray images. The result is a probabilistic segmentation that quantifies uncertainty, conditioned upon the model, so that measurements such as joint space can be made with associated uncertainty.