Analysis of functional MRI image using independent component analysis

  • Authors:

  • Minfen Shen;Weiling Xu;Jinxia Huang;Patch Beadle

  • Affiliations:

  • Key Lab. of Guangdong, Shantou University, Guangdong, China;Key Lab. of Guangdong, Shantou University, Guangdong, China;Key Lab. of Guangdong, Shantou University, Guangdong, China;School of System Engineering, Portsmouth University, Portsmouth, U.K.

  • Venue:
  • PDCAT'04 Proceedings of the 5th international conference on Parallel and Distributed Computing: applications and Technologies
  • Year:
  • 2004

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Abstract

To understand how the brain functions work and assist the diagnosis of the brain diseases, functional magnetic resonance imaging (fMRI) has been widely used. This paper proposes an effective scheme to deal with the issue of identifying the functional signals and suppress the background noise by using independent component analysis (ICA) technique. The fastICA is discussed and applied to solve the problem of blind separation of the source functional signals. Finally, an example with the real fMRI data set was carried out in terms of the proposed method. The experimental result demonstrated the effectiveness and the robustness of the ICA and the related blind source separation approach.