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Feature Selection Framework for White Matter Fiber Clustering Based on Normalized Cuts

  • Simon Koppers
  • Christoph Hebisch
  • Dorit Merhof
Part of the Informatik aktuell book series (INFORMAT)

Abstract

Due to its ability to automatically identify spatially and functionally related white matter fiber bundles, fiber clustering has the potential to improve our understanding of white matter anatomy. The normalized cuts (NCut) criterion has proven to be a suitable method for clustering fiber tracts. In this work, we show that the NCut value can be used for unsupervised feature selection as a measure for the quality of clustering. We further present a method how feature selection can be improved by penalizing spatially illogical clustering results, which is achieved by employing the Silhouette index for a fixed set of geometric features.

Keywords

Feature Selection Cluster Result Spectral Cluster Silhouette Index Unsupervised Feature Selection 
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 2016

Authors and Affiliations

  • Simon Koppers
    • 1
  • Christoph Hebisch
    • 1
  • Dorit Merhof
    • 1
  1. 1.Institute of Imaging & Computer VisionRWTH Aachen UniversityAachen

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