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Segmenting Multiple Sclerosis Lesions Using a Spatially Constrained K-Nearest Neighbour Approach

  • Mark Lyksborg
  • Rasmus Larsen
  • Per Soelberg Sørensen
  • Morten Blinkenberg
  • Ellen Garde
  • Hartwig R. Siebner
  • Tim Bjørn Dyrby
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7325)

Abstract

We propose a method for the segmentation of Multiple Sclerosis lesions. The method is based on probability maps derived from a K-Nearest Neighbours classification. These are used as a non parametric likelihood in a Bayesian formulation with a prior that assumes connectivity of neighbouring voxels. The formulation is solved using the method of Iterated Conditional Modes (ICM). The parameters of the method are found through leave-one-out cross validation on training data after which it is evaluated on previously unseen test data. The multi modal features investigated are 3 structural MRI modalities, the diffusion MRI measures of Fractional Anisotropy (FA), Mean Diffusivity (MD) and several spatial features. Results show a benefit from the inclusion of diffusion primarily to the most difficult cases. Results shows that combining probabilistic K-Nearest Neighbour with a Markov Random Field formulation leads to a slight improvement of segmentations.

Keywords

Fractional Anisotropy Near Neighbour Markov Random Field Multiple Sclerosis Lesion Iterate Conditional Mode 
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 2012

Authors and Affiliations

  • Mark Lyksborg
    • 1
  • Rasmus Larsen
    • 1
  • Per Soelberg Sørensen
    • 3
  • Morten Blinkenberg
    • 3
  • Ellen Garde
    • 2
  • Hartwig R. Siebner
    • 2
  • Tim Bjørn Dyrby
    • 2
  1. 1.Informatics and Mathematical ModelingTechnical University of DenmarkDenmark
  2. 2.Danish Research Centre for Magnetic ResonanceCopenhagen University HospitalHvidovreDenmark
  3. 3.Danish Multiple Sclerosis Research CenterUniversity of CopenhagenDenmark

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