Iterative graph cuts for image segmentation with a nonlinear statistical   shape prior
  Shape-based regularization has proven to be a useful method for delineating objects within noisy images where one has prior knowledge of the shape of the targeted object. When a collection of possible shapes is available, the specification of a shape prior using kernel density estimation is a natural technique. Unfortunately, energy functionals arising from kernel density estimation are of a form that makes them impossible to directly minimize using efficient optimization algorithms such as graph cuts. Our main contribution is to show how one may recast the energy functional into a form that is minimizable iteratively and efficiently using graph cuts. 
Joshua C. Chang and Tom Chou
cs.CV math.OC physics.data-an q-bio.QM stat.AP