Simultaneous searching of globally optimal interacting surfaces with shape priors

Multiple surface searching with only image intensity information is a difficult job in the presence of high noise and weak edges. We present in this paper a novel method for globally optimal multi-surface searching with a shape prior represented by convex pairwise energies. A 3-D graph-theoretic framework is employed. An arc-weighted graph is constructed based on a shape model built from training datasets. A wide spectrum of constraints is then incorporated. The shape prior term penalizes the local topological change from the original shape model. The globally optimal solution for multiple surfaces can be obtained by computing a maximum flow in low-order polynomial time. Compared with other graph-based methods, our approach provides more local and flexible control of the shape. We also prove that our algorithm can handle the detection of multiple crossing surfaces with no shared voxels. Our method was applied to several application problems, including medical image segmentation, scenic image segmentation, and image resizing. Compared with results without using shape prior information, our improvement was quite impressive, demonstrating the promise of our method.