Unsupervised Segmentation of Head Tissues from Multi-modal MR Images for EEG Source Localization
Journal article, 2014

In this paper, we present and evaluate an automatic unsupervised segmentation method, hierarchical segmenta- tion approach (HSA)–Bayesian-based adaptive mean shift (BAMS), for use in the construction of a patient-specific head conductivity model for electroencephalography (EEG) source localization. It is based on a HSA and BAMS for segmenting the tissues from multi-modal magnetic resonance (MR) head images. The evaluation of the proposed method was done both directly in terms of segmentation accuracy and indirectly in terms of source localization accuracy. The direct evaluation was performed relative to a commonly used reference method brain extraction tool (BET)–FMRIB’s automated segmenta- tion tool (FAST) and four variants of the HSA using both synthetic data and real data from ten subjects. The synthetic data includes multiple realizations of four different noise levels and several realizations of typical noise with a 20 % bias field level. The Dice index and Hausdorff distance were used to measure the segmentation accuracy. The indirect evaluation was performed relative to the reference method BET-FAST using synthetic two-dimensional (2D) multimodal magnetic resonance (MR) data with 3 % noise and synthetic EEG (generated for a prescribed source). The source localiza- tion accuracy was determined in terms of localization error and relative error of potential. The experimental results dem- onstrate the efficacy of HSA-BAMS, its robustness to noise and the bias field, and that it provides better segmentation accuracy than the reference method and variants of the HSA. They also show that it leads to a more accurate localization accuracy than the commonly used reference method and sug- gest that it has potential as a surrogate for expert manual segmentation for the EEG source localization problem.

Source localization

Multi-modal

Bayesian

Head tissues

Magnetic resonance imaging

Automatic segmentation

Mean shift

Electroencephalography (EEG)

Author

Mahmood Qaiser

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

Artur Chodorowski

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

Andrew Mehnert

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

Johanna Gellermann

Universitatsklinikum Tubingen Medizinische Fakultat

Mikael Persson

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

Journal of Digital Imaging

0897-1889 (ISSN) 1618-727X (eISSN)

Vol. 28 4 499-514

Subject Categories

Signal Processing

Medical Image Processing

DOI

10.1007/s10278-014-9752-6

More information

Created

10/7/2017