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Partial Clustering for Tissue Segmentation in MRI

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Advances in Neuro-Information Processing (ICONIP 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5507))

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Abstract

Magnetic resonance imaging (MRI) is a imaging and diagnostic tool widely used, with excellent spatial resolution, and efficient in distinguishing between soft tissues. Here, we present a method for semi-automatic identification of brain tissues in MRI, based on a combination of machine learning approaches. Our approach uses self-organising maps (SOMs) for voxel labelling, which are used to seed the discriminative clustering (DC) classification algorithm. This method reduces the intensive need for a specialist, and allows for a rather systematic follow-up of the evolution of brain lesions, or their treatment.

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Author information

Authors and Affiliations

  1. Adaptive Informatics Research Centre, Helsinki University of Technology, P.O. Box 5400, FIN-02015, Finland

    Nicolau Gonçalves, Janne Nikkilä & Ricardo Vigário

Authors
  1. Nicolau Gonçalves
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  2. Janne Nikkilä
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  3. Ricardo Vigário
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Editor information

Editors and Affiliations

  1. Network Design and Research Center, Kyushu Institute of Technology,, 680-4, Kawazu, Iizuka,, 820-8502, Fukuoka, Japan

    Mario Köppen

  2. Knowledge Engineering and Discovery Research Institute (KEDRI), School of Computing and Mathematical Sciences, Auckland University of Technology, 350 Queen Street, 10110, Auckland, New Zealand

    Nikola Kasabov

  3. Department of Electrical and Computer Engineering, Robotics Laboratory, Auckland University of Technology, 38 Princes Street,, 1142, Auckland, New Zealand

    George Coghill

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© 2009 Springer-Verlag Berlin Heidelberg

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Gonçalves, N., Nikkilä, J., Vigário, R. (2009). Partial Clustering for Tissue Segmentation in MRI. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03040-6_68

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  • DOI: https://doi.org/10.1007/978-3-642-03040-6_68

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03039-0

  • Online ISBN: 978-3-642-03040-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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