On the Fully Automatic Construction of a Realistic Head Model for EEG Source Localization
Paper in proceedings, 2013

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.

brain

segmentation

EEG

Author

Mahmood Qaiser

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Imaging and Image Analysis

Yazdan Shirvany

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Artur Chodorowski

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Imaging and Image Analysis

Johanna Gellermann

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Biomedical Electromagnetics

Fredrik Edelvik

Anders Hedström

University of Gothenburg

Mikael Persson

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

1557170X (ISSN)

3331-3334 6610254

Subject Categories

Signal Processing

DOI

10.1109/EMBC.2013.6610254

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7/2/2019 3