• DocumentCode
    1817821
  • Title

    Intensity-based shape propagation for volumetric image segmentation

  • Author

    Tan, E.T. ; Srinivasan, Rajagopalan ; Robb, R.A.

  • Author_Institution
    Biomedical Imaging Resource, Mayo Clinic, Rochester, MN
  • fYear
    2006
  • fDate
    6-9 April 2006
  • Firstpage
    738
  • Lastpage
    741
  • Abstract
    The shape propagation scheme robustly combines shape and edge information in two steps to perform volumetric image segmentation. The inward-propagating step performs shape interpolation from user-defined sparse segmentations. The edge estimation step improves the accuracy of interpolated boundaries using a Bayesian approach that handles the presence of edges or its lack of. The scheme was found to be robust in segmenting T-1 weighted MRI of the corpus callosum. The algorithm also runs in linear time. The efficiency and robustness of this scheme demonstrates significant potential for use in assisting tedious manual volumetric segmentation that may be performed in clinical applications
  • Keywords
    Bayes methods; biomedical MRI; image segmentation; interpolation; medical image processing; Bayesian approach; T-1 weighted MRI; corpus callosum; edge estimation; intensity-based shape propagation; shape interpolation; user-defined sparse segmentations; volumetric image segmentation; Bayesian methods; Biomedical imaging; Educational institutions; Flowcharts; Image edge detection; Image segmentation; Interpolation; Level set; Robustness; Shape;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Biomedical Imaging: Nano to Macro, 2006. 3rd IEEE International Symposium on
  • Conference_Location
    Arlington, VA
  • Print_ISBN
    0-7803-9576-X
  • Type

    conf

  • DOI
    10.1109/ISBI.2006.1625022
  • Filename
    1625022