Abstract
Energy functional minimization is a popular technique for medical image segmentation. The segmentation must be initialized, weights for competing terms of an energy functional must be tuned, and the functional minimized. There is a substantial amount of guesswork involved. We reduce this guesswork by analytically determining the optimal weights and minimizing a convex energy functional independent of the initialization. We demonstrate improved results over state of the art on a set of 470 clinical examples.
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