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Bayesian Model Selection for Pathological Data

  • Carole H. Sudre
  • Manuel Jorge Cardoso
  • Willem Bouvy
  • Geert Jan Biessels
  • Josephine Barnes
  • Sébastien Ourselin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)

Abstract

The detection of abnormal intensities in brain images caused by the presence of pathologies is currently under great scrutiny. Selecting appropriate models for pathological data is of critical importance for an unbiased and biologically plausible model fit, which in itself enables a better understanding of the underlying data and biological processes. Besides, it impacts on one’s ability to extract pathologically meaningful imaging biomarkers. With this aim in mind, this work proposes a fully unsupervised hierarchical model selection framework for neuroimaging data which permits the stratification of different types of abnormal image patterns without prior knowledge about the subject’s pathological status.

Keywords

Gaussian Mixture Model Markov Random Field White Matter Hyperintensities Multiple Sclerosis Lesion Tissue Class 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Carole H. Sudre
    • 1
    • 2
  • Manuel Jorge Cardoso
    • 1
    • 2
  • Willem Bouvy
    • 3
  • Geert Jan Biessels
    • 3
  • Josephine Barnes
    • 2
  • Sébastien Ourselin
    • 1
    • 2
  1. 1.Translational Imaging Group, CMICUniversity College LondonUK
  2. 2.Dementia Research CentreUCL Institute of NeurologyLondonUK
  3. 3.Department of Neurology and NeurosurgeryUMC UtrechtNetherlands

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