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

IEEE International Conference on Pattern Recognition (ICPR): 2254-2257, 2010
Download the publication : ParamEstim_paper.pdf [1.4Mo]   ParamEstim_poster.pdf [9.8Mo]  

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.

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BibTex references

@InProceedings{KSBR10,
  author       = "M.Keuper and T.Schmidt and J.Padeken and P.Heun and K.Palme and H.Burkhardt and O.Ronneberger",
  title        = "3D Deformable Surfaces with Locally Self-Adjusting Parameters - A Robust Method to Determine Cell Nucleus Shapes",
  booktitle    = "IEEE International Conference on Pattern Recognition (ICPR)",
  pages        = "2254-2257",
  year         = "2010",
  url          = "http://lmb.informatik.uni-freiburg.de//Publications/2010/KSBR10"
}

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