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Topology Noise Removal for Curve and Surface Evolution

  • Chao Chen
  • Daniel Freedman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6533)

Abstract

In cortex surface segmentation, the extracted surface is required to have a particular topology, namely, a two-sphere. We present a new method for removing topology noise of a curve or surface within the level set framework, and thus produce a cortical surface with correct topology. We define a new energy term which quantifies topology noise. We then show how to minimize this term by computing its functional derivative with respect to the level set function. This method differs from existing methods in that it is inherently continuous and not digital; and in the way that our energy directly relates to the topology of the underlying curve or surface, versus existing knot-based measures which are related in a more indirect fashion. The proposed flow is validated empirically.

Keywords

Active Contour Homology Class Active Contour Model Topology Control Signed Distance Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Chao Chen
    • 1
  • Daniel Freedman
    • 2
  1. 1.IST Austria (Institute of Science and Technology Austria)Vienna University of TechnologyAustria
  2. 2.Hewlett-Packard LaboratoriesIsrael

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