• DocumentCode
    1282728
  • Title

    Topology adaptive deformable surfaces for medical image volume segmentation

  • Author

    McInemey, T. ; Terzopoulos, Demetri

  • Author_Institution
    Dept. of Math., Phys. & Comput. Sci., Ryerson Polytech. Inst., Toronto, Ont., Canada
  • Volume
    18
  • Issue
    10
  • fYear
    1999
  • Firstpage
    840
  • Lastpage
    850
  • Abstract
    Deformable models, which include deformable contours (the popular snakes) and deformable surfaces, are a powerful model-based medical image analysis technique. The authors develop a new class of deformable models by formulating deformable surfaces in terms of an affine cell image decomposition (ACID). The authors´ approach significantly extends standard deformable surfaces, while retaining their interactivity and other desirable properties. In particular, the ACID induces an efficient reparameterization mechanism that enables parametric deformable surfaces to evolve into complex geometries, even modifying their topology as necessary. The authors demonstrate that their new ACID-based deformable surfaces, dubbed T-surfaces, can effectively segment complex anatomic structures from medical volume images.
  • Keywords
    image segmentation; medical image processing; modelling; topology; T-surfaces; affine cell image decomposition; complex anatomic structures; efficient reparameterization mechanism; medical diagnostic imaging; medical image volume segmentation; model-based medical image analysis technique; topology adaptive deformable surfaces; Biomedical imaging; Deformable models; Geometry; Image analysis; Image decomposition; Image reconstruction; Image segmentation; Shape; Surface reconstruction; Topology; Algorithms; Brain; Humans; Magnetic Resonance Angiography; Models, Neurological; Surface Properties;
  • fLanguage
    English
  • Journal_Title
    Medical Imaging, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0278-0062
  • Type

    jour

  • DOI
    10.1109/42.811261
  • Filename
    811261