Multi-domain, higher order level set scheme for 3D image segmentation on the GPU

Ojaswa Sharma, Qin Zhang, François Anton, Chandrajit Bajaj

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    Abstract

    Level set method based segmentation provides an efficient tool for topological and geometrical shape handling. Conventional level set surfaces are only $C^0$ continuous since the level set evolution involves linear interpolation to compute derivatives. Bajaj et al. present a higher order method to evaluate level set surfaces that are $C^2$ continuous, but are slow due to high computational burden. In this paper, we provide a higher order GPU based solver for fast and efficient segmentation of large volumetric images. We also extend the higher order method to multi-domain segmentation. Our streaming solver is efficient in memory usage.
    Original languageEnglish
    Title of host publicationProceedings of the IEEE Conference on Computer Vision and Pattern Recognition
    PublisherIEEE
    Publication date2010
    Pages1063-6919
    ISBN (Print)978-1-4244-6984-0
    DOIs
    Publication statusPublished - 2010
    EventIEEE Conference on Computer Vision and Pattern Recognition - San Francisco, USA
    Duration: 1 Jan 2010 → …
    Conference number: 23

    Conference

    ConferenceIEEE Conference on Computer Vision and Pattern Recognition
    Number23
    CitySan Francisco, USA
    Period01/01/2010 → …

    Keywords

    • Volume segmentation
    • level set method
    • CUDA
    • GPU computing
    • Higher order segmentation

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