• DocumentCode
    2480963
  • Title

    3D Deformable Surfaces with Locally Self-Adjusting Parameters - A Robust Method to Determine Cell Nucleus Shapes

  • Author

    Keuper, Margret ; Schmidt, Thorsten ; Padeken, Jan ; Heun, Patrick ; Palme, Klaus ; Burkhardt, Hans ; Ronneberger, Olaf

  • Author_Institution
    Comput. Sci. Dept., Freiburg Univ., Freiburg, Germany
  • fYear
    2010
  • fDate
    23-26 Aug. 2010
  • Firstpage
    2254
  • Lastpage
    2257
  • Abstract
    When using deformable models for the segmentation of biological data, the choice of the best weighting parameters for the internal and external forces is crucial. Especially when dealing with 3D fluorescence microscopic data and cells within dense tissue, object boundaries are sometimes not visible. In these cases, one weighting parameter set for the whole contour is not desirable. We are presenting a method for the dynamic adjustment of the weighting parameters, that is only depending on the underlying data and does not need any prior information. The method is especially apt to handle blurred, noisy, and deficient data, as it is often the case in biological microscopy.
  • Keywords
    image segmentation; medical image processing; nucleus; shape recognition; 3D deformable surfaces; 3D fluorescence microscopic data; biological data segmentation; biological microscopy; determine cell nucleus shapes; locally selfadjusting parameters; robust method; Biology; Covariance matrix; Force; Image segmentation; Microscopy; Shape; Three dimensional displays; active contour; cell segmentation; parameter estimation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Pattern Recognition (ICPR), 2010 20th International Conference on
  • Conference_Location
    Istanbul
  • ISSN
    1051-4651
  • Print_ISBN
    978-1-4244-7542-1
  • Type

    conf

  • DOI
    10.1109/ICPR.2010.552
  • Filename
    5595956