International Journal of Computer Vision

, Volume 117, Issue 2, pp 142–158 | Cite as

Data-Dependent Higher-Order Clique Selection for Artery–Vein Segmentation by Energy Minimization

  • Yoshiro Kitamura
  • Yuanzhong Li
  • Wataru Ito
  • Hiroshi Ishikawa
Article

Abstract

We propose a novel segmentation method based on energy minimization of higher-order potentials. We introduce higher-order terms into the energy to incorporate prior knowledge on the shape of the segments. The terms encourage certain sets of pixels to be entirely in one segment or the other. The sets can for instance be smooth curves in order to help delineate pulmonary vessels, which are known to run in almost straight lines. The higher-order terms can be converted to submodular first-order terms by adding auxiliary variables, which can then be globally minimized using graph cuts. We also determine the weight of these terms, or the degree of the aforementioned encouragement, in a principled way by learning from training data with the ground truth. We demonstrate the effectiveness of the method in a real-world application in fully-automatic pulmonary artery–vein segmentation in CT images.

Keywords

Segmentation Higher-order energy Artery–vein segmentation Surgery simulation 

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Yoshiro Kitamura
    • 1
    • 2
    • 3
  • Yuanzhong Li
    • 1
  • Wataru Ito
    • 1
  • Hiroshi Ishikawa
    • 2
    • 3
  1. 1.Imaging Technology CenterFujifilm CorporationTokyoJapan
  2. 2.Department of Computer Science and EngineeringWaseda UniversityTokyoJapan
  3. 3.JST CRESTSaitamaJapan

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