Elastic Distortion of Deformable Feature Maps for Fully-Automatic Segmentation of Multispectral MRI Data Sets of the Human Brain

  • Axel Wismüller
  • Frank Vietze
  • Dominik R. Dersch
  • Gerda Leinsinger
  • Johannes Behrends
  • Helge Ritter
  • Klaus Hahn
Conference paper

DOI: 10.1007/978-3-642-59757-2_22

Part of the book series Informatik aktuell (INFORMAT)
Cite this paper as:
Wismüller A. et al. (2000) Elastic Distortion of Deformable Feature Maps for Fully-Automatic Segmentation of Multispectral MRI Data Sets of the Human Brain. In: Horsch A., Lehmann T. (eds) Bildverarbeitung für die Medizin 2000. Informatik aktuell. Springer, Berlin, Heidelberg

Abstract

In this paper, we present an algorithm that provides adaptive plasticity in function approximation problems: the deformable (feature) map (DM) algorithm. The DM approach reduces a class of similar function approximation problems to the explicit supervised one-shot training of a single data set. This is followed by a subsequent, appropriate similarity transformation which is based on a self-organized deformation of the underlying multidimensional probability distributions. After discussing the theory of the DM algorithm, we present results of its application to the real-world problem of fully automatic voxel-based multispectral image segmentation, employing magnetic resonance data sets of the human brain.

Keywords

Algorithmen Magnetresonanz Segmentierung Neuronale Netze Vektorquantisierung 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Axel Wismüller
    • 1
  • Frank Vietze
    • 1
  • Dominik R. Dersch
    • 2
  • Gerda Leinsinger
    • 1
  • Johannes Behrends
    • 1
  • Helge Ritter
    • 3
  • Klaus Hahn
    • 1
  1. 1.Institut für Radiologische DiagnostikLudwig-Maximilians-Universität MünchenMünchenGermany
  2. 2.Crux Cybernetics CorpSydneyAustralia
  3. 3.AG NeuroinformatikUniversität BielefeldGermany