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Modelling of Magnetic Resonance Spectra Using Mixtures for Binned and Truncated Data

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Pattern Recognition and Image Analysis (IbPRIA 2007)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4478))

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Abstract

Magnetic Resonance Spectroscopy (MRS) provides the biochemical composition of a tissue under study. This information is useful for the in-vivo diagnosis of brain tumours. Prior knowledge of the relative position of the organic compound contributions in the MRS suggests the development of a probabilistic mixture model and its EM-based Maximum Likelihood Estimation for binned and truncated data. Experiments for characterizing and classifying Short Time Echo (STE) spectra from brain tumours are reported.

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Authors and Affiliations

  1. BET-IBM, Polytechnical University of Valencia,  

    Juan M. Garcia-Gomez & Montserrat Robles

  2. Katholieke Universiteit Leuven, Dept. of Electrical Engineering, ESAT-SCD(SISTA),  

    Sabine Van Huffel

  3. DSIC, Polytechnical University of Valencia,  

    Alfons Juan-Císcar

Authors
  1. Juan M. Garcia-Gomez
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  2. Montserrat Robles
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  3. Sabine Van Huffel
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  4. Alfons Juan-Císcar
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Editor information

Joan Martí José Miguel Benedí Ana Maria Mendonça Joan Serrat

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© 2007 Springer Berlin Heidelberg

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Garcia-Gomez, J.M., Robles, M., Van Huffel, S., Juan-Císcar, A. (2007). Modelling of Magnetic Resonance Spectra Using Mixtures for Binned and Truncated Data. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2007. Lecture Notes in Computer Science, vol 4478. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72849-8_34

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  • DOI: https://doi.org/10.1007/978-3-540-72849-8_34

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72848-1

  • Online ISBN: 978-3-540-72849-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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