title: Iterative graph cuts for image segmentation with a nonlinear statistical shape prior.
abstract: 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.
authors.affiliation: Mathematical Biosciences Institute, The Ohio State University, Jennings Hall, 3rd Floor, 1735 Neil Avenue, Columbus, Ohio 43210.
authors.forename: Joshua C
authors.initials: JC
authors.lastname: Chang
authors.affiliation: UCLA Biomathematics and Mathematics, BOX 951766, Room 5303 Life Sciences, Los Angeles, CA 90095-1766.
authors.forename: Tom
authors.initials: T
authors.lastname: Chou
keywords: Image segmentation; MM; energy minimization; graph cuts; kernel density estimation; statistical shape prior