Higher-order graph cuts and medical image segmentation

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Energy minimization is regularly used for medical image segmentation. Higher-order energies are perhaps not as common, but are nevertheless being used increasingly often. Whereas the common first- order (pairwise) potential can directly model only the relationship between pairs of pixels, the higher-order potential can model more complex and useful relationships between more than two variables. For instance, sets of pixels, chosen according to the shape to be segmented, can be encouraged to be entirely in one segment or the other by higher-order terms. In this talk, I will describe methods for minimizing higher-order energies using graph cuts as well as some real-world examples of their applications in medical image segmentation that have been deployed in commercial medical imaging software.

    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    PublisherSpringer Verlag
    Volume9555
    ISBN (Print)9783319302843
    Publication statusPublished - 2016
    Event7th Pacific-Rim Symposium on Image and Video Technology, PSIVT 2015 - Auckland, New Zealand
    Duration: 2015 Nov 232015 Nov 27

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume9555
    ISSN (Print)03029743
    ISSN (Electronic)16113349

    Other

    Other7th Pacific-Rim Symposium on Image and Video Technology, PSIVT 2015
    CountryNew Zealand
    CityAuckland
    Period15/11/2315/11/27

    Keywords

    • Graph cuts
    • Graphical model
    • Higher-order energy
    • Markov random fields
    • MRF
    • Segmentation

    ASJC Scopus subject areas

    • Computer Science(all)
    • Theoretical Computer Science

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  • Cite this

    Ishikawa, H. (2016). Higher-order graph cuts and medical image segmentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9555). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9555). Springer Verlag.