A Probabilistic Multi-phase Model for Variational Image Segmentation

  • Thomas Pock
  • Horst Bischof
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4174)


Recently, the Phase Field Method has shown to be a powerful tool for variational image segmentation. In this paper, we present a novel multi-phase model for probability based image segmentation. By interpreting the phase fields as probabilities of pixels belonging to a certain phase, we obtain the model formulation by maximizing the mutual information between image features and the phase fields. For optimizing the model, we derive the Euler Lagrange equations and present their efficient implementation by using a narrow band scheme. We present experimental results on segmenting synthetic, medical and natural images.


Mutual Information Image Segmentation Gaussian Mixture Model Active Contour 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 2006

Authors and Affiliations

  • Thomas Pock
    • 1
  • Horst Bischof
    • 1
  1. 1.Institute for Computer Graphics and Vision, Graz University of TechnologyGrazAustria

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