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Iterative Graph Cuts for Image Segmentation with a Nonlinear Statistical Shape Prior

  • Joshua C. Chang
  • Tom Chou
Article

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.

Keywords

Image segmentation MM Graph cuts Energy minimization Statistical shape prior Kernel density estimation 

Notes

Acknowledgements

This material is based upon work supported by the National Science Foundation under Agreement No. 0635561. J.C. and T.C. also acknowledge support from the National Science Foundation through grants DMS-1032131 and DMS-1021818, and from the Army Research Office through grant 58386MA.

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Mathematical Biosciences InstituteThe Ohio State UniversityColumbusUSA
  2. 2.UCLA Biomathematics and MathematicsLos AngelesUSA

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