One-class machine learning for brain activation detection

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2007, Pages

  Song, Xiaomu ^a   Iordanescu, George ^a   Wyrwicz, Alice M ^a  

^a NORTHWESTERN UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BRAIN; DATA REDUCTION; IMAGE CLASSIFICATION; MAGNETIC RESONANCE IMAGING; PROBLEM SOLVING;

COGNITIVE STATES; TEXTURE FEATURES;

LEARNING SYSTEMS;

EID: 34948892069     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2007.383339     Document Type: Conference Paper

References (25)

1. B.-H. Asa, H. David, T. S. Hava, and V. Vapnik. Support vector clustering. Journal of Machine Learning Research, 2:125-137, 2001.
2. W. Backfriender, R. Baumgartner, M. Samal, E. Moser, and H. Bergmann. Quantification of intensity variations in functional MR images using rotated principal components. Physics in Medicine and Biology, 41(8):1425-1438, 1996.
3. C.-C. Chang and C-J. Lin. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.twr cjlin/libsvm.
4. K. H. Chuang, M. J. Chiu, C. C. Lin, and J. H. Chen. Modelfree functional MRI analysis using Kohonen clustering neural network and fuzzy C means. IEEE Trans. on Medical Imaging, 18(12): 1117-1128, 1999.
5. B. V. Dasarathy, J. S. Sanchez, and S. Townsend. Nearest neighbour editing and condensing tools-synergy exploitation. Pattern Analysis & Applications, 3(1):19-30, 2000.
6. G. Davenport, T. A. Smith, and N. Pincever. Denoising functional MR images: A comparison of wavelet denoising and gaussian smoothing. IEEE Trans. on Medical Imaging, 23(3):374-387, March 2004.
7. T. Evgeniou, M. Pontil, C. Papageorgiou, and T. Poggio. Image representations and feature selection for multimedia database search. IEEE Trans. on Pattern Analysis and Machine Intelligence, 15(4):911-920, 2003.
8. K. J. Friston, A. P. Holmes, K. J. Worsley, J. B. Poline, C. Frith, and R. S. J. Frackowiak. Statistical parametric maps in functional imaging: A general linear approach. Human Brain Mapping, 2:189-210, 1995.
9. M. M. Galloway. Texture analysis using gray level run lengths. Computer Graphics and Image Processing, 4:172-179, 1975.
10. C. Goutte, P. Toft, E. Rostrup, F. A. Nielsen, and L. K. Hansen. On clustering fMRI time series. NeuroImage, 9:298-310, 1999.
11. S.-Z. Hamid, J. P. Donald, O. H. David, and R. L. Renee. Model-independent method for fMRI analysis. IEEE Trans. on Medical Imaging, 23(3):285-296, 2004.
12. D. R. Hardoon and L. M. Manevitz. fMRI analysis via one-class machine learning techniques. In Proc. of Conference on Data Mining, Systems Analysis and Optimization in Neuroscience, Florida, USA, 2006.
13. J. W. Jaromczyk and G. T. Toussaint. Relative neighborhood graphs and their relatives. Proceedings of the IEEE, 80(9): 1502-1517, Sept. 1992.
14. S. LaConte, S. Strother, V. Cherkassky, J. Anderson, and X. Hu. Support vector machines for temporal classification of block design fMRI data. Neurolmage, 26:317-329, 2005.
15. M. J. McKeown, S. Makeig, G. G. Brown, T. P. Jung, S.S. Kindermann, A. J. Bell, and T. J. Sejnowski. Analysis of fMRI data by blind separation into independent spatial components. Human Brain Mapping, 6:160-188, 1998.
16. T. Mitchell, R. Hutchinson, R. Niculescu, F.Pereira, X. Wang, M. Just, and S. Newman. Learning to decode cognitive states from brain images. Machine Learning, 57(1-2): 145-175, Oct. 2004.
17. S.-C. Ngan and X. Hu. Analysis of functional magnetic resonance imaging data using self-organizing mapping with spatial connectivity. Magn. Reson. Med., 41:936-946, 1999.
18. U. E. Ruttimann, M. Unser, R. R. Rawlings, D. Rio, N. F. Ramsey, V. S. Mattay, D. W. Hommer, J. A. Frank, and D. R. Weinberger. Statistical analysis of functional MRI data in the wavelet domain. IEEE Trans. on Medical Imaging, 17(2): 142-154, 1998.
19. B. Schölkopf, J. C. Platt, J. Shawe-Taylor, A. J. Smola, and R. C. Williamson. Estimating the support of a highdimensional distribution. Neural Computation, 13(7):1443-1471, 2001.
20. X. Song, M. Murphy, and A. M. Wyrwicz. Spatiotemporal denoising and clustering of fMRI data. In Proc. of the 13th IEEE International conference on Image Processing, Atlanta, GA, Oct. 2006.
21. C. Sun and W G. Wee. Neighboring gray level dependence matrix for texture classification. Computer Vision, Graphics, and Image Processing, 23:341-352, 1983.
22. D. M. J. Tax. One-class Classification. PhD thesis, Technische Universiteit Delft, The Netherlands, 2001.
23. V. N. Vapnik. Statistical Learning Theory. Wiley, 1998.
24. D. Wang, L. Shi, D. S. Yeung, P. A. Heng, T. T. Wong, and E. C. C. Tsang. Support vector clustering for brain activation detection. In Proc. of the 8th International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 572-579, Oct. 2005.
25. L. Zhang, D. Samaras, D. Tomasi, and N. Goldstein. Machine learning for clinical diagnosis from functional magnetic resonance imaging. In Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pages 1211-1217, 2005.