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
Link To Document