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Proceedings Paper

A hybrid skull-stripping algorithm based on adaptive balloon snake models
Author(s): Hung-Ting Liu; Tony W. H. Sheu; Herng-Hua Chang
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Paper Abstract

Skull-stripping is one of the most important preprocessing steps in neuroimage analysis. We proposed a hybrid algorithm based on an adaptive balloon snake model to handle this challenging task. The proposed framework consists of two stages: first, the fuzzy possibilistic c-means (FPCM) is used for voxel clustering, which provides a labeled image for the snake contour initialization. In the second stage, the contour is initialized outside the brain surface based on the FPCM result and evolves under the guidance of the balloon snake model, which drives the contour with an adaptive inward normal force to capture the boundary of the brain. The similarity indices indicate that our method outperformed the BSE and BET methods in skull-stripping the MR image volumes in the IBSR data set. Experimental results show the effectiveness of this new scheme and potential applications in a wide variety of skull-stripping applications.

Paper Details

Date Published: 25 February 2013
PDF: 7 pages
Proc. SPIE 8655, Image Processing: Algorithms and Systems XI, 86550J (25 February 2013); doi: 10.1117/12.2008462
Show Author Affiliations
Hung-Ting Liu, National Taiwan Univ. (Taiwan)
Tony W. H. Sheu, National Taiwan Univ. (Taiwan)
Herng-Hua Chang, National Taiwan Univ. (Taiwan)

Published in SPIE Proceedings Vol. 8655:
Image Processing: Algorithms and Systems XI
Karen O. Egiazarian; Sos S. Agaian; Atanas P. Gotchev, Editor(s)

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