  Minimization of boundary curvature is a classic regularization technique for
image segmentation in the presence of noisy image data. Techniques for
minimizing curvature have historically been derived from descent methods which
could be trapped in a local minimum and therefore required a good
initialization. Recently, combinatorial optimization techniques have been
applied to the optimization of curvature which provide a solution that achieves
nearly a global optimum. However, when applied to image segmentation these
methods required a meaningful data term. Unfortunately, for many images,
particularly medical images, it is difficult to find a meaningful data term.
Therefore, we propose to remove the data term completely and instead weight the
curvature locally, while still achieving a global optimum.
