Level Set Based Shape Prior Segmentation

  • Authors:
  • Tony Chan;Wei Zhu

  • Affiliations:
  • University of California at Los Angeles;New York University

  • Venue:
  • CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
  • Year:
  • 2005

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Abstract

We propose a level set based variational approach that incorporates shape priors into Chan-Veseýs model [3] for the shape prior segmentation problem. In our model, besides the level set function for segmentation, as in Cremersý work [5], we introduce another labelling level set function to indicate the regions on which the prior shape should be compared. Our model can segment an object, whose shape is similar to the given prior shape, from a background where there are several objects. Moreover, we provide a proof for a fast solution principle, which was mentioned [7] and similar to the one proposed in [19], for minimizingChan-Veseýs segmentation model without length term. We extend the principle to the minimization of our prescribed functionals.