NDLI: On the fully automatic construction of a realistic head model for EEG source localization

Content Provider	IEEE Xplore Digital Library
Author	Mahmood, Q. Shirvany, Y. Mehnert, A. Chodorowski, A. Gellermann, J. Edelvik, F. Hedstrom, A. Persson, M.
Copyright Year	2013
Description	Author affiliation: Univ.klinik fur Radioonkologie, Tubingen, Germany (Gellermann, J.) \|\| Fraunhofer-Chalmers Res. Center, Goteborg, Sweden (Edelvik, F.) \|\| Dept. of Signals & Syst., Chalmers Univ. of Technol., Goteborg, Sweden (Mahmood, Q.; Shirvany, Y.; Mehnert, A.; Chodorowski, A.; Persson, M.) \|\| Sahlgrenska Acad., Goteborg, Sweden (Hedstrom, A.)
Abstract	Accurate multi-tissue segmentation of magnetic resonance (MR) images is an essential first step in the construction of a realistic finite element head conductivity model (FEHCM) for electroencephalography (EEG) source localization. All of the segmentation approaches proposed to date for this purpose require manual intervention or correction and are thus laborious, time-consuming, and subjective. In this paper we propose and evaluate a fully automatic method based on a hierarchical segmentation approach (HSA) incorporating Bayesian-based adaptive mean-shift segmentation (BAMS). An evaluation of HSA-BAMS, as well as two reference methods, in terms of both segmentation accuracy and the source localization accuracy of the resulting FEHCM is also presented. The evaluation was performed using (i) synthetic 2D multi-modal MRI head data and synthetic EEG (generated for a prescribed source), and (ii) real 3D T1-weighted MRI head data and real EEG data (with expert determined source localization). Expert manual segmentation served as segmentation ground truth. The results show that HSA-BAMS outperforms the two reference methods and that it can be used as a surrogate for manual segmentation for the construction of a realistic FEHCM for EEG source localization.
Starting Page	3331
Ending Page	3334
File Size	2074174
Page Count	4
File Format	PDF
ISBN	9781457702167
ISSN	1557170X
DOI	10.1109/EMBC.2013.6610254
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electroencephalography Brain modeling Image segmentation Head Magnetic resonance imaging Hidden Markov models Manuals
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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