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On the Fully Automa...
On the Fully Automatic Construction of a Realistic Head Model for EEG Source Localization
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- Qaiser, Mahmood, 1981 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Shirvany, Yazdan, 1980 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Chodorowski, Artur, 1959 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Gellermann, Johanna, 1967 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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Edelvik, Fredrik, 1972 (författare)
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- Hedström, Anders (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för neurovetenskap och fysiologi, sektionen för klinisk neurovetenskap och rehabilitering,Institute of Neuroscience and Physiology, Department of Clinical Neuroscience and Rehabilitation,University of Gothenburg
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- Persson, Mikael, 1959 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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(creator_code:org_t)
- 2013
- 2013
- Engelska.
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Ingår i: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. Osaka, Japan, 3-7 July 2013. - 1557-170X. - 9781457702167 ; , s. 3331-3334
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)
Nyckelord
- brain
- segmentation
- EEG
- brain
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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