Authors:
Aleksandar Jeremic
1
;
D. Nikolic
2
;
G. Djuricic
3
;
N. Milcanovic
3
and
Z. Jokovic
3
Affiliations:
1
Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada
;
2
University Children’s Hospital, Faculty of Medicine, University of Belgrade, Serbia
;
3
Department of Radiology, University Children’s Hospital, Belgrade, School of Medicine, University of Belgrade, Serbia
Keyword(s):
Source Localization, Electroencephalography, Inverse Models.
Abstract:
Successful imaging of electrical activity in newborn infants is highly dependent on accurate and/or adequate representation of head representation from structural point of view. Namely, the electrical activity and the corresponding electroencephalography (EEG) measurements are dependant on electrical properties of brain and skull tissue i.e. corresponding conductivities and geometry of the skull and brain. Automated procedure for geometry/structural analysis are sparse even for adults and almost non-existent for neonates and newborn infants. In this paper we propose to develop automatic procedures for analyzing skull geometry and potentially other shapes/sizes that are relevant for electrical imaging of the cortex activity. To this purpose we propose to estimate the thickness of the skull using magnetic resonance (MR) images as a preliminary step in obtaining/estimating relevant structural parameters. Since the number of MR images is rather limited due to the age of the patients we d
evelop a semi-supervised machine learning algorithm in which certain number of MR slices is used for training. We demonstrate applicability of our preliminary results using real MR images obtained from the University Children’s Hospital, University of Belgrade, Serbia.
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