Knowledge-based image segmentation using sparse shape priors and high-order MRFs

Overview & Synopsis

In this thesis, we propose a novel framework for knowledge-based segmentation using high-order Markov Random Fields (MRFs). We represent the shape model as a point distribution graphical model which encodes pose invariant shape priors through L1 sparse higher order cliques. Each triplet clique encodes the local shape variation statistics on the angle measurements which inherit invariance to global transformations (i.e. translation,rotation and scale). A sparse higher-order graph structure is learned through MRF training using dual decomposition, producing boosting efficiency while preserving its ability to represent the shape variation.We incorporate the prior knowledge in a novel framework for model-based segmentation.We address the segmentation problem as a maximum a posteriori (MAP) estimation in a probabilistic framework. A global MRF energy function is defined to jointly combine regional statistics, boundary support as well as shape prior knowledge for estimating the optimal model parameters (i.e. the positions of the control points). The pose-invariant priors are encoded in second-order MRF potentials, while regional statistics acting on a derived image feature space can be exactly factorized using Divergence theorem. Furthermore, we propose a novel framework for joint model-pixel segmentation towardsa more refined segmentation when exact boundary delineation is of interest. Aunified model-based and pixel-driven integrated graphical model is developed to combine both top-down and bottom-up modules simultaneously. The consistency between the model and the image space is introduced by a model decomposition which associates the model parts with pixels labeling. Both of the considered higher-order MRFs are optimized efficiently using state-of the-art MRF optimization algorithms. Promising results on computer vision and medical image applications demonstrate the potential of the proposed segmentation methods.