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Conf Proc IEEE Eng Med Biol Soc. 2012;2012:5319-22. doi: 10.1109/EMBC.2012.6347195.

Adaptive image segmentation for robust measurement of longitudinal brain tissue change.

Author information

  • 1Imaging of Dementia and Aging (IDeA) Laboratory, Department of Neurology, University of California, Davis, CA95616. USA. evanfletcher@gmail.com

Abstract

We present a method that significantly improves magnetic resonance imaging (MRI) based brain tissue segmentation by modeling the topography of boundaries between tissue compartments. Edge operators are used to identify tissue interfaces and thereby more realistically model tissue label dependencies between adjacent voxels on opposite sides of an interface. When applied to a synthetic MRI template corrupted by additive noise, it provided more consistent tissue labeling across noise levels than two commonly used methods (FAST and SPM5). When applied to longitudinal MRI series it provided lesser variability in individual trajectories of tissue change, suggesting superior ability to discriminate real tissue change from noise. These results suggest that this method may be useful for robust longitudinal brain tissue change estimation.

PMID:
23367130
PMCID:
PMC3776590
DOI:
10.1109/EMBC.2012.6347195
[PubMed - indexed for MEDLINE]
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