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arXiv · 1208.4384

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

Joshua C. Chang·Tom Chou
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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.

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BibTeXRIS

Joshua C. Chang, Tom Chou. 2012-08-21. Iterative graph cuts for image segmentation with a nonlinear statistical shape prior. https://doi.org/10.1007/s10851-013-0440-9

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