Research Article | OPEN ACCESS
Detection of Brain Activity in Functional Magnetic Resonance Imaging Data using Matrix Factorization
1Amir A. Khaliq, 2I.M. Qureshi, 1Ihsanulhaq and 1Jawad A. Shah
1Department of Electronic Engineering, International Islamic University Islamabad
2Department of Electronic Engineering Air University, ISSS Islamabad, Pakistan
Research Journal of Applied Sciences, Engineering and Technology 2013 24:5566-5571
Received: October 12, 2012 | Accepted: December 03, 2012 | Published: May 30, 2013
Abstract
Non-negative matrix factorization (NMF) is becoming a popular tool for decomposition of data in the field of signal and image processing like Independent Component Analysis (ICA). In this study we are relaxing the requirement of non-negative data for NMF making the update equations simple and thus making it Matrix Factorization (MF) and implementing it on simulated Functional Magnetic Resonance Imaging (fMRI) data for detection of neuronal activity. Simulated fMRI data is processed to detect the hidden sources of task related activity, functional activity and artifacts using the proposed MF technique. Performance of the proposed scheme is better than NMF in terms of average correlation results of the extracted sources/time courses with the actual sources/time courses. Similarly proposed MF is computationally cost effective and converges fast as compared to NMF. Also extracted sources obey no permutation which is the limitation of ICA and NMF.
Keywords:
Blind source separation, Functional Magnetic Resonance Imaging (fMRI), matrix factorization, medical image processin,
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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