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Smoothness Prior Information in Principal Component Analysis of Dynamic Image Data

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Information Processing in Medical Imaging (IPMI 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2082))

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Abstract

Principal component analysis is a well developed and under- stood method of multivariate data processing. Its optimal performance requires knowledge of noise covariance that is not available in most ap- plications. We suggest a method for estimation of noise covariance based on assumed smoothness of the estimated dynamics.

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Author information

Authors and Affiliations

  1. Institute of Information Theory and Automation, Academy of Sciences of the Czech Republic, POB 18, CZ-182 08, Prague 8, Czech Republic

    Václav Šmídl & Miroslav Kárný

  2. Charles University Prague, Czech Republic

    Martin Šámal

  3. Institute of Biomedical Engineering and Physics, AKH, Vienna, Austria

    Werner Backfrieder

  4. Johns Hopkins University, Baltimore, MD, USA

    Zsolt Szabo

Authors
  1. Václav Šmídl
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  2. Miroslav Kárný
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  3. Martin Šámal
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  4. Werner Backfrieder
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  5. Zsolt Szabo
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Editor information

Editors and Affiliations

  1. Biomedical Engineering, University of California, One Shields Avenue, Davis, CA, 95616, USA

    Michael F. Insana

  2. Signal and Image Processing Institute, University of Southern California, 3740 McClintock Avenue, Los Angeles, CA, 90089-2564, USA

    Richard M. Leahy

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© 2001 Springer-Verlag Berlin Heidelberg

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Šmídl, V., Kárný, M., Šámal, M., Backfrieder, W., Szabo, Z. (2001). Smoothness Prior Information in Principal Component Analysis of Dynamic Image Data. In: Insana, M.F., Leahy, R.M. (eds) Information Processing in Medical Imaging. IPMI 2001. Lecture Notes in Computer Science, vol 2082. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45729-1_24

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  • DOI: https://doi.org/10.1007/3-540-45729-1_24

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42245-7

  • Online ISBN: 978-3-540-45729-9

  • eBook Packages: Springer Book Archive

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