Shape regularized active contour using iterative global search and local optimization

Recently, nonlinear shape models have been shown to improve the robustness and flexibility of segmentation. In this paper, we propose shape regularized active contour (ShRAC) that incorporates existing nonlinear shape models into the classical active contour approach. ShRAC uses a discrete representation of the contour to allow efficient combinatorial search. The search for optimal contour is performed by coarse-to-fine algorithm that iterates between combinatorial search and gradient-based local optimization. First, multi-solution dynamic programming (MSDP) is used to generate initial candidates by minimizing only the image energy. In the second step, a combination of image energy and shape energy determined by a given prior shape model is minimized for the initial candidates using a local optimization method and the best one is selected. To have diverse initial candidates, we employ a clustered solution pruning procedure in the MSDP search space. Finally, local shape regularization is used to feed shape constraints back into the new MSDP search space of the next iteration. Our search strategy combines the advantages of global combinatorial search and local optimization, and has shown excellent robustness to local minima caused by distracting suboptimal segmentations. Experimental results on segmentation of different anatomical structures using ShRAC are provided.