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Unsupervised regularized classification of multi-spectral MRI

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Visualization in Biomedical Computing (VBC 1996)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1131))

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Abstract

In this paper, we present a framework for automatic classification of multi-spectral MRI data. We propose a novel clustering method using a contextual hypothesis which succeeds in discriminating largely overlapping component distributions. The initial classifications obtained for each channel in a multi-spectral study independently are subsequently merged by minimizing a Minimum Description Length (MDL) criterion trading the data-fit accuracy for simplicity of the model (number of classes). In a final stage, we use a refined Markovian prior to regularize the final segmentation. This prior preserves fine structures and linear shapes as opposed to the typically used Ising or Potts MRF priors. This work represents work-in-progress. Results on a limited number of data are presented.

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Authors and Affiliations

  1. Laboratory for Medical Imaging Research, ESAT-Radiology U. Z. Gasthuisberg, Radiology, Herestraat 49, B-3000, Leuven, Belgium

    D. Vandermeulen, X. Descombes, P. Suetens & G. Marchal

Authors
  1. D. Vandermeulen
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  2. X. Descombes
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  3. P. Suetens
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  4. G. Marchal
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Editor information

Karl Heinz Höhne Ron Kikinis

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© 1996 Springer-Verlag Berlin Heidelberg

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Vandermeulen, D., Descombes, X., Suetens, P., Marchal, G. (1996). Unsupervised regularized classification of multi-spectral MRI. In: Höhne, K.H., Kikinis, R. (eds) Visualization in Biomedical Computing. VBC 1996. Lecture Notes in Computer Science, vol 1131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0046958

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  • DOI: https://doi.org/10.1007/BFb0046958

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61649-8

  • Online ISBN: 978-3-540-70739-4

  • eBook Packages: Springer Book Archive

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