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Gaussian Mixture Models for Brain Activation Detection from fMRI Data

Garg, G, Prasad, G, Garg, L and Coyle, DH (2011) Gaussian Mixture Models for Brain Activation Detection from fMRI Data. In: Symp. on Noninvasive Functional Source Imaging of the Brain & Heart and the 8th Intl. Conference on Bioelectromagnetism (NFSI & ICBEM 2011), Banff, Canada. NFSI & ICBEM 2011. 6 pp. [Conference contribution]

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Abstract

Gaussian Mixture Model (GMM) based clustering has been successfully used in various types of medical and image data analysis, because of its robustness and stability under high noise levels. GMMs are employed in this work to extract the activation patterns from functional Magnetic Resonance Imaging (fMRI) data. The highly correlated time-series obtained with a given stimulus has been used to find the voxels contributing to the Blood Oxygenation Level Dependent (BOLD) activation regions. GMM clustering has been used for modeling of various activation patterns considering the strength, delay and duration of the epochs. A synthetic dataset and a real dataset provided by the Wellcome Trust Centre for Neuroimaging, University College London, UK are used to demonstrate the superiority of this approach in automating the process of identifying activated brain regions.

Item Type:Conference contribution (Paper)
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Information Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
ID Code:18564
Deposited By: Professor Girijesh Prasad
Deposited On:19 May 2011 11:39
Last Modified:09 Dec 2015 10:57

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