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An Adaptive Mean-Shift Framework for MRI Brain Segmentation.
- Source :
- IEEE Transactions on Medical Imaging; Aug2009, Vol. 28 Issue 8, p1238-1250, 13p, 9 Diagrams, 5 Charts, 6 Graphs
- Publication Year :
- 2009
-
Abstract
- An automated scheme for magnetic resonance imaging (MRI) brain segmentation is proposed. An adaptive mean-shift methodology is utilized in order to classify brain voxels into one of three main tissue types: gray matter, white matter, and Cerebro-spinal fluid. The MRI image space is represented by a high-dimensional feature space that includes multimodal intensity features as well as spatial features. An adaptive mean-shift algorithm clusters the joint spatial-intensity feature space, thus extracting a representative set of high-density points within the feature space, otherwise known as modes. Tissue segmentation is obtained by a follow-up phase of intensity-based mode clustering into the three tissue categories. By its nonparametric nature, adaptive mean-shift can deal successfully with nonconvex clusters and produce convergence modes that are better candidates for intensity based classification than the initial voxels. The proposed method is validated on 3-D single and multimodal datasets, for both simulated and real MRI data. It is shown to perform well in comparison to other state-of-the-art methods without the use of a preregistered statistical brain atlas. [ABSTRACT FROM AUTHOR]
- Subjects :
- BRAIN
MAGNETIC resonance imaging
CENTRAL nervous system
CONSCIOUSNESS
INTELLECT
Subjects
Details
- Language :
- English
- ISSN :
- 02780062
- Volume :
- 28
- Issue :
- 8
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Medical Imaging
- Publication Type :
- Academic Journal
- Accession number :
- 43694578
- Full Text :
- https://doi.org/10.1109/TMI.2009.2013850