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Liver Segmentation in CT Images for Intervention Using a Graph-Cut Based Model

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Book cover Abdominal Imaging. Computational and Clinical Applications (ABD-MICCAI 2011)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7029))

Abstract

Liver segmentation in computerized tomography (CT) images has been widely studied in recent years, of which the graph cut models demonstrate a great potential with the advantage of global optima and practical efficiency. In this paper, a graph-cut based model for liver CT segmentation is presented. The image is interpreted as a graph, that the segmentation problem is then casted as an optimal cut on the graph. An energy function is then formulated for minimization, which combines both regional properties and boundary smoothness. The prior knowledge on liver is unified into the energy function via fuzzy similarity measure. Finally, the optimal cut can be computed through the max-flow algorithm. Experiments on a variety of CT images show its effectiveness and efficiency.

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Author information

Authors and Affiliations

  1. Research Center of CAD, Tongji University, Shanghai, 200092, China

    Yufei Chen, Weidong Zhao, Qidi Wu, Zhicheng Wang & Jinyong Hu

  2. The Engineering Research Center for Enterprise Digital Technology, Ministry of Education, Tongji University, Shanghai, 200092, China

    Yufei Chen, Weidong Zhao, Qidi Wu, Zhicheng Wang & Jinyong Hu

Authors
  1. Yufei Chen
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  2. Weidong Zhao
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  3. Qidi Wu
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  4. Zhicheng Wang
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  5. Jinyong Hu
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Editor information

Editors and Affiliations

  1. Department of Radiology, Massachusetts General Hospital, 25 New Chardon Street, Suite 400C, 02114, Boston, MA, USA

    Hiroyuki Yoshida

  2. Fraunhofer-Institut für Graphische Datenverarbeitung (IGD), Abteilung Cognitive Computing and Medical Imaging, Fraunhoferstraße 5, 64283, Darmstadt, Gemany

    Georgios Sakas

  3. Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Medical Center, 111 Michigan Avenue, NW, 20010-2916, Washington, DC, USA

    Marius George Linguraru

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© 2012 Springer-Verlag Berlin Heidelberg

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Chen, Y., Zhao, W., Wu, Q., Wang, Z., Hu, J. (2012). Liver Segmentation in CT Images for Intervention Using a Graph-Cut Based Model. In: Yoshida, H., Sakas, G., Linguraru, M.G. (eds) Abdominal Imaging. Computational and Clinical Applications. ABD-MICCAI 2011. Lecture Notes in Computer Science, vol 7029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28557-8_20

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  • DOI: https://doi.org/10.1007/978-3-642-28557-8_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28556-1

  • Online ISBN: 978-3-642-28557-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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