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Prior Knowledge, Random Walks and Human Skeletal Muscle Segmentation

  • P. -Y. Baudin
  • N. Azzabou
  • P. G. Carlier
  • Nikos Paragios
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7510)

Abstract

In this paper, we propose a novel approach for segmenting the skeletal muscles in MRI automatically. In order to deal with the absence of contrast between the different muscle classes, we proposed a principled mathematical formulation that integrates prior knowledge with a random walks graph-based formulation. Prior knowledge is represented using a statistical shape atlas that once coupled with the random walks segmentation leads to an efficient iterative linear optimization system. We reveal the potential of our approach on a challenging set of real clinical data.

Keywords

Random Walk Segmentation Result Prior Model Empirical Variance Partial Contour 
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 2012

Authors and Affiliations

  • P. -Y. Baudin
    • 1
    • 2
    • 3
    • 4
    • 5
    • 6
    • 7
  • N. Azzabou
    • 5
    • 6
    • 7
  • P. G. Carlier
    • 5
    • 6
    • 7
  • Nikos Paragios
    • 2
    • 3
    • 4
  1. 1.Siemens HealthcareSaint DenisFrance
  2. 2.Center for Visual ComputingEcole Centrale de ParisFrance
  3. 3.LIGM (UMR CNRS), Center for Visual Computing, Ecole des Ponts ParisTechUniversité Paris-EstFrance
  4. 4.Equipe Galen, INRIA SaclayIle-de-FranceFrance
  5. 5.Institute of MyologyParisFrance
  6. 6.I2BM, MIRCen, IdM NMR LaboratoryCEAParisFrance
  7. 7.UPMC University Paris 06ParisFrance

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