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Efficient Metropolis-Hasting Image Analysis for the Location of Vascular Entity

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Pattern Recognition (GCPR 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8753))

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Abstract

In this paper we present a novel approach for probabilistically exploring and modeling vascular networks in 3D angiograms. For modeling the vascular morphology and topology a graph-like particle model is used. Each particle represents the intrinsic properties of a small fraction of a vessel including position, orientation and scale. Explicit connections between particles determine the network topology. In evaluation using simulated as well as real X-ray and time-of-flight MRI angiograms the proposed method was able to accurately model the vascular network.

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Notes

  1. 1.

    FARSIGHT Project: http://www.farsight-toolkit.org/

  2. 2.

    AneuriskWeb project http://ecm2.mathcs.emory.edu/aneuriskweb

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Acknowledgment

This work was supported by Bioinformatics for Brain Sciences under the Strategic Research Program for Brain Sciences, MEXT (Japan). The work of M. Reisert is supported by the Deutsche Forschungsgemeinschaft (DFG), grant RE 3286/2-1.

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Authors and Affiliations

  1. Graduate School of Informatics, Kyoto University, Kyoto, Japan

    Henrik Skibbe & Shin Ishii

  2. Department of Radiology, University Hospital Freiburg, Freiburg, Germany

    Marco Reisert

Authors
  1. Henrik Skibbe
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  2. Marco Reisert
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  3. Shin Ishii
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Corresponding author

Correspondence to Henrik Skibbe .

Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Science, University of Münster, Münster, Germany

    Xiaoyi Jiang

  2. Computer Science Department 5, University of Erlangen-Nürnberg, Erlangen, Germany

    Joachim Hornegger

  3. Department of Computer Science, University of Kiel, Kiel, Germany

    Reinhard Koch

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Skibbe, H., Reisert, M., Ishii, S. (2014). Efficient Metropolis-Hasting Image Analysis for the Location of Vascular Entity. In: Jiang, X., Hornegger, J., Koch, R. (eds) Pattern Recognition. GCPR 2014. Lecture Notes in Computer Science(), vol 8753. Springer, Cham. https://doi.org/10.1007/978-3-319-11752-2_34

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  • DOI: https://doi.org/10.1007/978-3-319-11752-2_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11751-5

  • Online ISBN: 978-3-319-11752-2

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