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Joint Tumor Segmentation and Dense Deformable Registration of Brain MR Images

  • Sarah Parisot
  • Hugues Duffau
  • Stéphane Chemouny
  • Nikos Paragios
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7511)

Abstract

In this paper we propose a novel graph-based concurrent registration and segmentation framework. Registration is modeled with a pairwise graphical model formulation that is modular with respect to the data and regularization term. Segmentation is addressed by adopting a similar graphical model, using image-based classification techniques while producing a smooth solution. The two problems are coupled via a relaxation of the registration criterion in the presence of tumors as well as a segmentation through a registration term aiming the separation between healthy and diseased tissues. Efficient linear programming is used to solve both problems simultaneously. State of the art results demonstrate the potential of our method on a large and challenging low-grade glioma data set.

Keywords

Target Image Markov Random Field Pairwise Constraint Tumor Segmentation Deformable Registration 
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

  • Sarah Parisot
    • 1
    • 2
    • 3
  • Hugues Duffau
    • 4
  • Stéphane Chemouny
    • 3
  • Nikos Paragios
    • 1
    • 2
  1. 1.Center for Visual ComputingEcole Centrale ParisChatenay MalabryFrance
  2. 2.Equipe GALENINRIA Saclay - Ile de FranceOrsayFrance
  3. 3.Intrasense SASMontpellierFrance
  4. 4.Département de NeurochirurgieHopital Gui de Chauliac, CHU MontpellierFrance

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