SOM-Based wavelet filtering for the exploration of medical images

  • Authors:

  • Birgit Lessmann;Andreas Degenhard;Preminda Kessar;Linda Pointon;Michael Khazen;Martin O. Leach;Tim W. Nattkemper

  • Affiliations:

  • Theoretical Physics, University of Bielefeld, Bielefeld, Germany;Theoretical Physics, University of Bielefeld, Bielefeld, Germany;Clinical MR Research Group, Institute of Cancer Research, Royal Marsden Hospital, Sutton, Surrey, UK;Clinical MR Research Group, Institute of Cancer Research, Royal Marsden Hospital, Sutton, Surrey, UK;Clinical MR Research Group, Institute of Cancer Research, Royal Marsden Hospital, Sutton, Surrey, UK;Clinical MR Research Group, Institute of Cancer Research, Royal Marsden Hospital, Sutton, Surrey, UK;Applied Neuroinformatics Group, University of Bielefeld, Bielefeld, Germany

  • Venue:
  • ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
  • Year:
  • 2005

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Abstract

In medical image analysis there are many applications that require the definition of characteristic image features. Especially computationally generated characteristic image features have potential for the exploration of large datasets. In this work, we propose a method for investigating time series of medical images using a combination of the Discrete Wavelet Transform and the Self Organizing Map. Our approach allows relevant image information to be identified in wavelet space. This enables us to develop a filter algorithm suitable to find and extract the characteristic image features and to suppress interfering non-relevant image information.