A Conditional Random Field Approach for Coupling Local Registration with Robust Tissue and Structure Segmentation

  • Benoit Scherrer
  • Florence Forbes
  • Michel Dojat
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5762)


We consider a general modelling strategy to handle in a unified way a number of tasks essential to MR brain scan analysis. Our approach is based on the explicit definition of a Conditional Random Field (CRF) model decomposed into components to be specified according to the targeted tasks. For a specific illustration, we define a CRF model that combines robust-to-noise and to nonuniformity Markovian tissue and structure segmentations with local affine atlas registration. The evaluation performed on both phantoms and real 3T images shows good results and, in particular, points out the gain in introducing registration as a model component. Besides, our modeling and estimation scheme provide general guidelines to deal with complex joint processes for medical image analysis.


Markov Random Field Conditional Random Field Medical Image Analysis Tissue Segmentation Markov Random Field Model 
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 2009

Authors and Affiliations

  • Benoit Scherrer
    • 1
    • 3
  • Florence Forbes
    • 2
    • 3
  • Michel Dojat
    • 1
    • 3
  1. 1.INSERM, U836GrenobleFrance
  2. 2.INRIA, MISTISGrenobleFrance
  3. 3.Université Joseph FourierGrenobleFrance

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