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Enhanced spatial priors for segmentation of magnetic resonance imagery

  • Tina Kapur
  • W. Eric
  • L. Grimson
  • Ron Kikinis
  • William M. Wells
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1496)

Abstract

A Bayesian, model-based method for segmentation of Magnetic Resonance images is proposed. A discrete vector valued Markov Random Field model is used as a regularizing prior in a Bayesian classification algorithm to minimize the effect of salt-and-pepper noise common in clinical scans. The continuous Mean Field solution to the MRP is recovered using an Expectation-Maximization algorithm, and is a probabilistic segmentation of the image. A separate model is used to encode the relative geometry of structures, and as a spatially varying prior in the Bayesian classifier. Preliminary results are presented for the segmentation of white matter, gray matter, fluid, and fat in Gradient Echo MR images of the brain.

Keywords

White Matter Markov Random Field Hide Variable Tissue Class Relative Geometry 
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 1998

Authors and Affiliations

  • Tina Kapur
    • 1
  • W. Eric
    • 1
  • L. Grimson
    • 1
  • Ron Kikinis
    • 2
  • William M. Wells
    • 1
    • 2
  1. 1.MIT AI LaboratoryCambridgeUSA
  2. 2.Brigham & Womens HospitalHarvard Medical SchoolBostonUSA

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