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Journal of Biomedical Optics

T1 magnetic resonance imaging head segmentation for diffuse optical tomography and electroencephalography
Author(s): Katherine L. Perdue; Solomon G. Diamond
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Paper Abstract

Accurate segmentation of structural magnetic resonance images is critical for creating subject-specific forward models for functional neuroimaging source localization. In this work, we present an innovative segmentation algorithm that generates accurate head tissue layer thicknesses that are needed for diffuse optical tomography (DOT) data analysis. The presented algorithm is compared against other publicly available head segmentation methods. The proposed algorithm has a root mean square scalp thickness error of 1.60 mm, skull thickness error of 1.96 mm, and summed scalp and skull error of 1.49 mm. We also introduce a segmentation evaluation metric that evaluates the accuracy of tissue layer thicknesses in regions of the head where optodes are typically placed. The presented segmentation algorithm and evaluation metric are tools for improving the localization accuracy of neuroimaging with DOT, and also multimodal neuroimaging such as combined electroencephalography and DOT.

Paper Details

Date Published: 14 February 2014
PDF: 9 pages
J. Biomed. Opt. 19(2) 026011 doi: 10.1117/1.JBO.19.2.026011
Published in: Journal of Biomedical Optics Volume 19, Issue 2
Show Author Affiliations
Katherine L. Perdue, Thayer School of Engineering at Dartmouth (United States)
Solomon G. Diamond, Thayer School of Engineering at Dartmouth (United States)


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