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Shape-based regularization has proven to be a useful method for delineatingobjects within noisy images where one has prior knowledge of the shape of thetargeted object. When a collection of possible shapes is available, thespecification of a shape prior using kernel density estimation is a naturaltechnique. Unfortunately, energy functionals arising from kernel densityestimation are of a form that makes them impossible to directly minimize usingefficient optimization algorithms such as graph cuts. Our main contribution isto show how one may recast the energy functional into a form that isminimizable iteratively and efficiently using graph cuts.

Iterative graph cuts for image segmentation with a nonlinear statistical shape prior