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3D Knowledge-Based Segmentation Using Pose-Invariant Higher-Order Graphs

  • Chaohui Wang
  • Olivier Teboul
  • Fabrice Michel
  • Salma Essafi
  • Nikos Paragios
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6363)

Abstract

Segmentation is a fundamental problem in medical image analysis. The use of prior knowledge is often considered to address the ill-posedness of the process. Such a process consists in bringing all training examples in the same reference pose, and then building statistics. During inference, pose parameters are usually estimated first, and then one seeks a compromise between data-attraction and model-fitness with the prior model. In this paper, we propose a novel higher-order Markov Random Field (MRF) model to encode pose-invariant priors and perform 3D segmentation of challenging data. The approach encodes data support in the singleton terms that are obtained using machine learning, and prior constraints in the higher-order terms. A dual-decomposition-based inference method is used to recover the optimal solution. Promising results on challenging data involving segmentation of tissue classes of the human skeletal muscle demonstrate the potentials of the method.

Keywords

Markov Random Field Factor Graph Active Shape Model Point Distribution Model Clique Potential 
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 2010

Authors and Affiliations

  • Chaohui Wang
    • 1
    • 2
  • Olivier Teboul
    • 1
    • 3
  • Fabrice Michel
    • 1
    • 2
  • Salma Essafi
    • 1
    • 2
  • Nikos Paragios
    • 1
    • 2
  1. 1.Laboratoire MASEcole Centrale de ParisFrance
  2. 2.Equipe GALENINRIA Saclay - Île de FranceOrsayFrance
  3. 3.MicrosoftFrance

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