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

Yoshiro Kitamura, Yuanzhong Li, Wataru Ito, Hiroshi Ishikawa

    Research output: Contribution to journalArticlepeer-review

    10 Citations (Scopus)


    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.

    Original languageEnglish
    JournalInternational Journal of Computer Vision
    Publication statusAccepted/In press - 2015 Sep 14


    • Artery–vein segmentation
    • Higher-order energy
    • Segmentation
    • Surgery simulation

    ASJC Scopus subject areas

    • Software
    • Artificial Intelligence
    • Computer Vision and Pattern Recognition

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