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Proceedings Paper

Multiresolutional graph cuts for brain extraction from MR images
Author(s): Yong-Sheng Chen; Li-Fen Chen; Yi-Ting Wang
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Paper Abstract

This paper presents a multiresolutional brain extraction framework which utilizes graph cuts technique to classify head magnetic resonance (MR) images into brain and non-brain regions. Starting with an over-extracted brain region, we refine the segmentation result by trimming non-brain regions in a coarse-to-fine manner. The extracted brain at the coarser level will be propagated to the finer level to estimate foreground/background seeds as constraints. The short-cut problem of graph cuts is reduced by the proposed pre-determined foreground from the coarser level. In order to consider the impact of the intensity inhomogeneities, we estimate the intensity distribution locally by partitioning volume images of each resolution into different numbers of smaller cubes. The graph cuts method is individually applied for each cube. Compared with four existing methods, the proposed method performs well in terms of sensitivity and specificity in our experiments for performance evaluation.

Paper Details

Date Published: 19 July 2013
PDF: 5 pages
Proc. SPIE 8878, Fifth International Conference on Digital Image Processing (ICDIP 2013), 88783B (19 July 2013); doi: 10.1117/12.2031540
Show Author Affiliations
Yong-Sheng Chen, National Chiao Tung Univ. (Taiwan)
Li-Fen Chen, National Yang-Ming Univ. (Taiwan)
Yi-Ting Wang, National Chiao Tung Univ. (Taiwan)


Published in SPIE Proceedings Vol. 8878:
Fifth International Conference on Digital Image Processing (ICDIP 2013)
Yulin Wang; Xie Yi, Editor(s)

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