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Computational and Mathematical Methods in Medicine
Volume 2014 (2014), Article ID 726712, 6 pages
http://dx.doi.org/10.1155/2014/726712
Research Article

A Novel Blind Separation Method in Magnetic Resonance Images

1School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China
2Key Laboratory of Integrated Electronic System, Ministry of Education, Chengdu 611731, China
3Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu 610072, China
4School of Computer Science & Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
5Southwest China Research Institute of Electronic Equipment, Chengdu 610036, China

Received 22 November 2013; Accepted 17 January 2014; Published 23 February 2014

Academic Editor: Yuanjie Zheng

Copyright © 2014 Jianbin Gao et al. 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.

Linked References

  1. A. Hyvärinen, J. Karhunen, and E. Oja, Independent Component Analysis, John Wiley & Sons, 2001.
  2. A. Bell and T. Sejnowski, “An information-maximization approach to blind separation and blind deconvolution,” Neural Computation, vol. 7, no. 6, pp. 1004–1034, 1995. View at Google Scholar · View at Scopus
  3. E. Oja and M. D. Plumbley, “Blind separation of positive sources by globally convergent gradient search,” Neural Computation, vol. 16, no. 9, pp. 1811–1825, 2004. View at Publisher · View at Google Scholar · View at Scopus
  4. A. Hyvärinen, “Fast and robust fixed-point algorithms for independent component analysis,” IEEE Transactions on Neural Networks, vol. 10, no. 3, pp. 626–634, 1999. View at Publisher · View at Google Scholar · View at Scopus
  5. W.-Q. Guo and T.-S. Qiu, “Adaptive blind estimation of evoked potentials in EEG based on minimum dispersion coefficient and revolving transform,” Chinese Journal of Biomedical Engineering, vol. 26, no. 5, pp. 647–651, 2007. View at Google Scholar · View at Scopus
  6. M. D. Plumbley and E. Oja, “A “nonnegative PCA” algorithm for independent component analysis,” IEEE Transactions on Neural Networks, vol. 15, no. 1, pp. 66–76, 2004. View at Publisher · View at Google Scholar · View at Scopus
  7. M. D. Plumbley, “Conditions for nonnegative independent component analysis,” IEEE Signal Processing Letters, vol. 9, no. 6, pp. 177–180, 2002. View at Publisher · View at Google Scholar · View at Scopus
  8. A. Cichocki and S.-I. Amar, Adaptive Blind Signal and Image Processing, John Wiley & Sons, 2002.
  9. M. Ye, Y. Liu, M. Liu, F. Li, and Q. Liu, “Blind image extraction by using local smooth information,” in Proceedings of the 5th International Conference on Natural Computation, vol. 3, pp. 415–420, Tianjin, China, 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. J. Gao and M. Ye, “Blind separation of locally smooth images based on genetic algorithm,” Journal of Computational Information Systems, vol. 6, no. 8, pp. 2465–2472, 2010. View at Google Scholar · View at Scopus
  11. L. Guo and M. Garland, “The use of entropy minimization for the solution of blind source separation problems in image analysis,” Pattern Recognition, vol. 39, no. 6, pp. 1066–1073, 2006. View at Publisher · View at Google Scholar · View at Scopus
  12. H. Tamura, T. Mori, and T. Yamawaki, “Textural features corresponding to visual perception,” IEEE Transactions on Systems, Man and Cybernetics, vol. 8, no. 6, pp. 460–473, 1978. View at Publisher · View at Google Scholar · View at Scopus
  13. Y. Zeng and M. Garland, “An improved algorithm for estimating pure component spectra in exploratory chemometric studies based on entropy minimization,” Analytica Chimica Acta, vol. 359, no. 3, pp. 303–310, 1998. View at Publisher · View at Google Scholar · View at Scopus
  14. J. E. Baker, “Adaptive selection methods for genetic algorithms,” in Proceedings of the 1st International Conference on Genetic Algorithms (ICGA '85), pp. 101–111, 1985.
  15. ftp://medical.nema.org/medical/Dicom/Multiframe/.
  16. http://brainweb.bic.mni.mcgill.ca/brainweb/anatomic_normal.html.
  17. S. Makeig, A. J. Bell, T. P. Jung, and T. J. Sejnowski, “Independent component analysis of electroencephalographic data,” Advances in Neural Information Processing Systems, pp. 145–151, 1996. View at Google Scholar
  18. M. Kawakatsu, “Application of ICA to MEG noise reduction,” in Proceedings of the 4th International Symposium on Independent Component Analysis and Blind Signal Separation, Nara, Japan, 2003.
  19. Y. S. Abu-Mostafa, “Independent component analysis in financial data,” in Computational Finance, The MIT Press, 2000. View at Google Scholar
  20. V. D. Calhoun T, Adali, L. K. Hansen, J. Larsen, and J. J. Pekar, “ICA of functional MRI data: an overview,” in Proceedings of the 4th International Symposium on Independent Component Analysis and Blind Signal Separation, Nara, Japan, 2003.
  21. M. J. McKeown and T. J. Sejnowski, “Independent component analysis of fMRI data: examining the assumptions,” Human Brain Mapping, vol. 6, no. 5-6, pp. 368–372, 1998. View at Google Scholar
  22. A. Hyvärinen and E. Oja, “Independent component analysis: algorithms and applications,” Neural Networks, vol. 13, no. 4-5, pp. 411–430, 2000. View at Publisher · View at Google Scholar · View at Scopus