Fully Bayesian joint model for MR brain scan tissue and structure segmentation.

@article{Scherrer2008FullyBJ,
  title={Fully Bayesian joint model for MR brain scan tissue and structure segmentation.},
  author={Benoit Scherrer and Florence Forbes and Catherine Garbay and Michel Dojat},
  journal={Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention},
  year={2008},
  volume={11 Pt 2},
  pages={1066-74}
}
In most approaches, tissue and subcortical structure segmentations of MR brain scans are handled globally over the entire brain volume through two relatively independent sequential steps. We propose a fully Bayesian joint model that integrates local tissue and structure segmentations and local intensity distributions. It is based on the specification of three conditional Markov Random Field (MRF) models. The first two encode cooperations between tissue and structure segmentations and integrate… CONTINUE READING
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Convergence theorems of Generalized Alternating Minimization Procedures

  • W. Byrne, A. Gunawardana
  • J. Machine Learning Research 6
  • 2005
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Magnetic resonance image tissue classification using a partial volume model

  • D Shattuck
  • NeuroImage 13(5)
  • 2001
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