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
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