Skull-Stripping Brain MR Images Using an Adaptive Balloon Snake Model
Date Issued
2012
Date
2012
Author(s)
Liu, Hung-Ting
Abstract
Brain image segmentation, also known as skull-stripping, has been the focus of a wide variety of research in recent years. This paper proposes a new automatic parametric active contour model (snake) for brain image extraction. The proposed framework consists of two stages: image preprocessing and image segmentation. First, the fuzzy possibilistic c-means (FPCM) is used for voxel clustering, which provides a labeled image for the following contour initialization. At the second stage, the contour is initialized outside the brain surface based on the result of the FPCM and evolves under the guidance of the balloon force. The balloon snake model drives the contour with an adaptive inward normal force to capture the boundary of the brain. The proposed algorithm is evaluated by segmenting a number of T1-weighted magnetic resonance images. Experimental results and comparisons with other existing approaches show the effectiveness of this new scheme and potential applications in a wide variety of brain image segmentation.
Subjects
Skull-stripping
image segmentation
active contour model
fuzzy possibilistic c-means
Type
thesis
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