• DocumentCode
    2084160
  • Title

    Vessel Crawlers: 3D Physically-based Deformable Organisms for Vasculature Segmentation and Analysis

  • Author

    McIntosh, Chris ; Hamarneh, Ghassan

  • Author_Institution
    Simon Fraser University, Canada
  • Volume
    1
  • fYear
    2006
  • fDate
    17-22 June 2006
  • Firstpage
    1084
  • Lastpage
    1091
  • Abstract
    We present a novel approach to the segmentation and analysis of vasculature from volumetric medical image data. Our method is an adoption and significant extension of deformable organisms, an artificial life framework for medical image analysis that complements classical deformable models with high-level, anatomically-driven control mechanisms. We extend deformable organisms to 3D, model their bodies as tubular spring-mass systems, and equip them with a new repertoire of sensory modules, behavioral routines, and decision making strategies. The result is a new breed of robust deformable organisms, vessel crawlers, that crawl along vasculature in 3D images, accurately segmenting vessel boundaries, detecting and exploring bifurcations, and providing sophisticated, clinically-relevant structural analysis. We validate our method through the segmentation and analysis of vascular structures in both noisy synthetic and real medical image data.
  • Keywords
    Bifurcation; Biomedical imaging; Crawlers; Decision making; Deformable models; Image analysis; Image segmentation; Medical control systems; Organisms; Robustness;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on
  • ISSN
    1063-6919
  • Print_ISBN
    0-7695-2597-0
  • Type

    conf

  • DOI
    10.1109/CVPR.2006.329
  • Filename
    1640871