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Biennial International Conference on Information Processing in Medical Imaging

IPMI 2005: Information Processing in Medical Imaging pp 234–245Cite as

Topology Preserving Tissue Classification with Fast Marching and Topology Templates

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3565))

Abstract

This paper presents a novel approach for object segmentation in medical images that respects the topological relationships of multiple structures as given by a template. The algorithm combines advantages of tissue classification, digital topology, and level-set evolution into a topology-invariant multiple-object fast marching method. The technique can handle any given topology and enforces object-level relationships with little constraint over the geometry. Applied to brain segmentation, it sucessfully extracts gray matter and white matter structures with the correct spherical topology without topology correction or editing of the sub-cortical structures.

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Author information

Authors and Affiliations

  1. Laboratory of Medical Image Computing, Neuroradiology Division, Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, MD, 21287, USA

    Pierre-Louis Bazin & Dzung L. Pham

Authors
  1. Pierre-Louis Bazin
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  2. Dzung L. Pham
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, The University of Iowa, IA 52242, Iowa City

    Gary E. Christensen

  2. Department of Electrical and Computer Engineering, University of Iowa, IA 52242, Iowa City, USA

    Milan Sonka

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© 2005 Springer-Verlag Berlin Heidelberg

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Bazin, PL., Pham, D.L. (2005). Topology Preserving Tissue Classification with Fast Marching and Topology Templates. In: Christensen, G.E., Sonka, M. (eds) Information Processing in Medical Imaging. IPMI 2005. Lecture Notes in Computer Science, vol 3565. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11505730_20

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  • DOI: https://doi.org/10.1007/11505730_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26545-0

  • Online ISBN: 978-3-540-31676-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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