Image segmentation with a shape prior based on simplified skeleton

In the paper we propose a new deformable shape model that is based on simplified skeleton graph. Such shape model allows to account for different shape variations and to introduce global constraints like known orientation or scale of the object. We combine the model with low-level image segmentation techniques based on Markov random fields and derive an approximate algorithm for the minimization of the energy function by performing stochastic coordinate descent. Experiments on two different sets of images confirm that usage of proposed shape model as a prior leads to improved segmentation quality.