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
Part of the Informatik aktuell book series (INFORMAT)

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

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