Open Access

Independent Component Analysis for Magnetic Resonance Image Analysis

  • Yen-Chieh Ouyang1,
  • Hsian-Min Chen1,
  • Jyh-Wen Chai2, 3, 4,
  • Cheng-Chieh Chen1,
  • Clayton Chi-Chang Chen4, 5Email author,
  • Sek-Kwong Poon6,
  • Ching-Wen Yang7 and
  • San-Kan Lee8
EURASIP Journal on Advances in Signal Processing20082008:780656

Received: 11 October 2007

Accepted: 30 December 2007

Published: 29 January 2008


Independent component analysis (ICA) has recently received considerable interest in applications of magnetic resonance (MR) image analysis. However, unlike its applications to functional magnetic resonance imaging (fMRI) where the number of data samples is greater than the number of signal sources to be separated, a dilemma encountered in MR image analysis is that the number of MR images is usually less than the number of signal sources to be blindly separated. As a result, at least two or more brain tissue substances are forced into a single independent component (IC) in which none of these brain tissue substances can be discriminated from another. In addition, since the ICA is generally initialized by random initial conditions, the final generated ICs are different. In order to resolve this issue, this paper presents an approach which implements the over-complete ICA in conjunction with spatial domain-based classification so as to achieve better classification in each of ICA-demixed ICs. In order to demonstrate the proposed over-complete ICA, (OC-ICA) experiments are conducted for performance analysis and evaluation. Results show that the OC-ICA implemented with classification can be very effective, provided the training samples are judiciously selected.

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Authors’ Affiliations

Department of Electrical Engineering, National Chung Hsing University, Taichung, Taiwan
Department of Radiology, College of Medicine, China Medical University, Taichung, Taiwan
School of Medicine, National Yang-Ming University, Taipei, Taiwan
Department of Radiology, Taichung Veterans General Hospital, Taichung, Taiwan
Department of Medical Imaging and Radiological Science, Central Taiwan University of Science and Technology, Taichung, Taiwan
Division of Gastroenterology, Department of Internal Medicine, Center of Clinical Informatics Research Development, Taichung Veterans General Hospital, Taichung, Taiwan
Computer Center, Taichung Veterans General Hospital, Taichung, Taiwan
Chia-Yi, Veterans Hospital, Chia-Yi, Taiwan


© Yen-Chieh Ouyang et al. 2008

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.