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Fast algorithms for fitting multiresolution hybrid shape models to brain 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 new and fast algorithms for shape recovery from brain MRI using the multiresolution hybrid shape models introduced in Vemuri and Radisavljevic. We present three new computational methods for model fitting to data and demonstrate the efficiency of our methods via experiments on brain MRI data.

This work was supported in part by the Whitaker Foundation and the NSF grant ECS-9210648.

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Author information

Authors and Affiliations

  1. Department of Computer & Information Science & Engg, University of Florida, 32611, Gainesville, Fl.

    B. C. Vemuri

  2. Department of Electrical & Computer Engg, University of Florida, 32611, Gainesville, Fl.

    Y. Guo

  3. Department of Neuroscience, University of Florida, 32611, Gainesville, Fl.

    C. M. Leonard

  4. Siemens Corporate Research, Princeton, NJ

    S. H. Lai

Authors
  1. B. C. Vemuri
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  2. Y. Guo
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  3. S. H. Lai
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  4. C. M. Leonard
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Editor information

Karl Heinz Höhne Ron Kikinis

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© 1996 Springer-Verlag Berlin Heidelberg

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Vemuri, B.C., Guo, Y., Lai, S.H., Leonard, C.M. (1996). Fast algorithms for fitting multiresolution hybrid shape models to brain 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/BFb0046956

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  • DOI: https://doi.org/10.1007/BFb0046956

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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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