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A conditional random field approach for coupling local registration with robust tissue and structure segmentation: A CRF Approach for Coupling Local Registration

Benoît Scherrer 1 Florence Forbes 2 Michel Dojat 1, *
* Corresponding author
2 MISTIS [2007-2015] - Modelling and Inference of Complex and Structured Stochastic Systems [2007-2015]
Grenoble INP [2007-2019] - Institut polytechnique de Grenoble - Grenoble Institute of Technology [2007-2019], LJK [2007-2015] - Laboratoire Jean Kuntzmann [2007-2015], Inria Grenoble - Rhône-Alpes
Abstract : 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.
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https://www.hal.inserm.fr/inserm-00517880
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Submitted on : Wednesday, September 15, 2010 - 6:44:10 PM
Last modification on : Monday, July 20, 2020 - 9:16:01 AM
Long-term archiving on: : Friday, December 2, 2016 - 10:44:42 AM

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Benoît Scherrer, Florence Forbes, Michel Dojat. A conditional random field approach for coupling local registration with robust tissue and structure segmentation: A CRF Approach for Coupling Local Registration. MICCAI 2009 - 12th International Conference on Medical Image Computing and Computer Assisted Intervention, Sep 2009, London, United Kingdom. pp.540-548, ⟨10.1007/978-3-642-04271-3_66⟩. ⟨inserm-00517880⟩

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