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
Link To Document :
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