A hybrid skull-stripping algorithm based on adaptive balloon snake models

Hung-Ting Liu; Tony W. H. Sheu; Herng-Hua Chang

doi:10.1117/12.2008462

25 February 2013 A hybrid skull-stripping algorithm based on adaptive balloon snake models

Hung-Ting Liu, Tony W. H. Sheu, Herng-Hua Chang

Proceedings Volume 8655, Image Processing: Algorithms and Systems XI; 86550J (2013) https://doi.org/10.1117/12.2008462
Event: IS&T/SPIE Electronic Imaging, 2013, Burlingame, California, United States

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.

Citation Download Citation

Hung-Ting Liu, Tony W. H. Sheu, and Herng-Hua Chang "A hybrid skull-stripping algorithm based on adaptive balloon snake models", Proc. SPIE 8655, Image Processing: Algorithms and Systems XI, 86550J (25 February 2013); https://doi.org/10.1117/12.2008462

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