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
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.
Images and movies
BibTex reference
@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" }