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Bildverarbeitung für die Medizin 2011 pp 104–108Cite as

Liver Vessel Segmentation Using Gradient Vector Flow

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Part of the book series: Informatik aktuell ((INFORMAT))

Abstract

An accurate segmentation of vascular systems is fundamental for many medical applications. Stability against different contrast levels and noise are very important. In this paper we propose an approach for the segmentation of the vascular system of the liver. It is based on the gradient vector flow (GVF) and Frangis vesselness measure. This method avoids multi-scale analysis and related scale space problems. It was evaluated on ten CT data-sets.

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References

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Author information

Authors and Affiliations

  1. Division of Medical and Biological Informatics, DKFZ Heidelberg, Heidelberg, Germany

    Caspar J. Goch, Xin Wang, Hans-Peter Meinzer & Ingmar Wegner

Authors
  1. Caspar J. Goch
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  2. Xin Wang
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  3. Hans-Peter Meinzer
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  4. Ingmar Wegner
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Corresponding author

Correspondence to Caspar J. Goch .

Editor information

Editors and Affiliations

  1. , Institut für Medizinische Informatik, Universität zu Lübeck, Ratzeburger Allee 160, Lübeck, 23562, Germany

    Heinz Handels

  2. , Institut für Medizinische Informatik, Universität Lübeck, Ratzeburger Allee 160, Lübeck, 23538, Germany

    Jan Ehrhardt

  3. Inst. Medizinische Informatik, RWTH Aachen, Pauwelsstr. 30, Aachen, 52074, Germany

    Thomas M. Deserno

  4. Abt. Med./Biol.Inform./H0100, Deutsches Krebsforschungzentrum (DKFZ), Im Neuenheimer Feld 280, Heidelberg, 69120, Germany

    Hans-Peter Meinzer

  5. Charité Campus Benjamin Franklin, Inst. Medizinische Informatik,, Universitätsklinikum Berlin, Hindenburgdamm 30, Berlin, 12200, Berlin, Germany

    Thomas Tolxdorff

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© 2011 Springer-Verlag Berlin Heidelberg

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Goch, C.J., Wang, X., Meinzer, HP., Wegner, I. (2011). Liver Vessel Segmentation Using Gradient Vector Flow. In: Handels, H., Ehrhardt, J., Deserno, T., Meinzer, HP., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2011. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19335-4_23

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