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

• Published in: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2013; pp. 3331 - 3334
• Main Authors: Mahmood, Qaiser, Shirvany, Yazdan, Mehnert, Andrew, Chodorowski, Artur, Gellermann, Johanna, Edelvik, Fredrik, Hedstrom, Anders, Persson, Mikael
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.01.2013
• Subjects: Algorithms; Automation; Bayes Theorem; brain; Brain modeling; Databases as Topic; EEG; Electroencephalography; Head; Head - anatomy & histology; Hidden Markov models; Humans; Image segmentation; Magnetic Resonance Imaging; Manuals; Models, Anatomic; segmentation; Signal Processing; Signalbehandling
• ISBN: 1457702169; 9781457702167
• ISSN: 1094-687X; 1557-170X
• DOI: 10.1109/EMBC.2013.6610254

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.