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Exploring the Effectiveness of Combining ICA with K-means and Fuzzy C-means in Brain Tissue Classification
|關鍵字:||K-means;K-平均;Fuzzy C-means;Independent Component Analysis;Fisher Linear Discriminant Analysis;模糊C平均;獨立成分分析;費雪線性辨別分析||出版社:||電機工程學系所||引用:|| G.A. Wright, “Magnetic resonance imaging,” IEEE Signal Processing Magazine, 14(1): 56-66, 1997  C.-I Chang, Hyperspectral Imaging: Techniques for Spectral Detection and Classification, Kluwer Academic Publishers, 2003.  L. Clarke, R. Vethuizen, M. Camacho, J. Heine, M. Vaidyanathan, L.O. Hall, R.W. Thatcher and M.L. Silbiger, “MRI segmentation: methods and applications,” Magnetic Resonance Imaging, 13(3): 343-368, 1995.  J.C. Bezdek, L.O. Hall and L. Clarke, “Review of MRI segmentation techniques using pattern recognition,” Medical Physics, 20: 1033-1048, 1993.  S. Theodoridis, K. Koutroumbas. Pattern recognition. 2nd, New York, Academic Press 2003; 411-415.  K. Alsabti, S. Ranka, V. Singh, “An Efficient K-Means Clustering Algorithm”  K. Alsabti, S.Ranka, and V.Singh. “An Efficient K-means Clustering Algorithm”. http://www.csie.ufl.edu/ranka/, 1997.  P. Dulykarn, Y. Rangsanseri, “Fuzzy C-means Clustering Using Spatial Information With Application to Remote Sensing”, Department of Telecommunications Engineering, Faculty of Engineering, 2001.  A. Hyvarinen, J. Karhunen and E. Oja, Independent Component Analysis, New York: John Wiley & Sons, 2001.  T. Nakai, S. Muraki, E. Bagarinao, Y. Miki, Y. Takehara, K. Matsuo, C. Kato, H. Sakahara, H. Isoda, “Application of independent component analysis to magnetic resonance imaging for enhancing the contrast of gray and white matter,” NeuroImage, 21: 251-260, 2004.  T.Kim, I.Lee, T.W. Lee, “Independent vector analysis: definition and algorithms.” Proc. of 40th Asilomar Conference on Signals, Systems and Computers, 2006. ACSSC ''06; 1393-1396.  T. Nakai, S. Muraki, E. Bagarinao, Y. Miki, Y. Takehara, K. Matsuo, C. Kato, H. Sakahara, H. Isoda, “Application of independent component analysis to magnetic resonance imaging for enhancing the contrast of gray and white matter,” NeuroImage, 21: 251-260, 2004.  Y.C. Ouyang, H.M. Chen, J.W. Chai, C.C.C. Chen, C.C. Chen, S.K. Poon, C.W. Yang, and S.K. Lee,“Independent Component Analysis for Magnetic Resonance Image Analysis.” EURASIP Journal on Applied Signal Processing, 780656:1-14, 2008.  Y.C. Ouyang, H.M. Chen, J.W. Chai, C.C.C. Chen, S.K. Poon, C.W. Yang, S.K. Lee, and C.I . Chang, “Band Expansion-Based Over-Complete Independent Component Analysis for Multispectral Processing of Magnetic Resonance Images.” IEEE Transactions on Biomedical Engineering, 55(6): 1666-1677, 2008J.W. Boardman, F.A. Kruse and R.O. Green, “Mapping target signatures via partial unmixing of AVIRIS data,” Summaries of JPL Airborne Earth Science Workshop, Pasadena, CA, 1995.  Y.J Chiou, H.M Chen, J.W Chai, C.C.C Chen, Y.C Ouyang, W.C Su, C.W Yang, S.K Lee, C.I Chang, “Brian Tissue Classification Using Independent Vector Analysis(IVA) For Magnetic Resonance Image” IEEE International Conference on Bioinformatics and Bioengineering, 52: 324-329, 2009  C.-I Chang and Q. Du, "Estimation of number of spectrally distinct signal sources in hyperspectral imagery," IEEE Trans. on Geoscience and Remote Sensing, vol. 42, no. 3, pp. 608-619, March 2008.  A. Ifarraguerri and C.-I Chang, "Hyperspectral image segmentation with convex cones," IEEE Trans. on Geoscience and Remote Sensing, vol. 37, no 2, pp. 756-770, March 1999.  S. Haykin, Neural Networks: A Comprehensive Foundation, 2nd ed., Prentice-Hall, 1999, Chapter 6.  R.O. Duda and P.O. Hart, Pattern Classification and Scene Analysis, New York: John Wiley & Sons, 1973.  Boardman, 1992, SIPS User's Guide Spectral Image Processing System, Version 1.2, Center for the Study of Earth from Space, Boulder, CO. 88 pp.  Kruse, F. A.; Lefkoff, A. B.; Boardman, J. W.; Heiedbrecht; Shapiro, A. T.; Barloon, P. J.; Goetz, A. F. H.,1992. The Spectral Image Processing System (SIPS) - Software for Integrated Analysis of AVIRIS DataSummaries of the 4th Annual JPL Airborne Geoscience Workshop, JPL Pub-92-14, AVIRISWorkshop. Jet Propulsion Laboratory, Pasadena, CA, pp. 23-25.  H. Soltanian-Zadeh, J.P. Windham and D.J. Peck, “Optimal linear transformation for MRI feature extraction,” IEEE Transaction on Medical Imaging, 15(6): 749-767, 1996.  J. C. Harsanyi, Detection and Classification of Subpixel Spectral Signatures in Hyperspectral Image Sequences, Department of Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD, August 1993.  Gonzalez, R. C., Woods, R. E., and Eddins, Digital Image Processing Using MATLAB, Prentice Hall, Upper Saddle River, NJ, 2004; 164-165.  http://www.bic.mni.mcgill.ca/brainweb, first date: May, 1997; last modified date: Jun, 2006.||摘要:||
本論文將敘述兩種被廣泛應用的方法，稱為K-means與Fuzzy C-means，使多種影像可以被分類為二維影像。首先，我們使用獨立成份分析來強化原始影像中的成份以減少雜訊影響。但如果原始影像有雜訊成份，在做完K-means與Fuzzy C-means分類之後的影像都很明顯可以看出雜訊成份。我們利用數次Fisher’s Linear Discriminate Analysis來實現分類來得到最後結果。實驗結果將驗證我們提出的方法在非監督式分類中將可達到我們預期的效果。
This paper presents an application of using a widely used clustering algorithm, called k-means and fuzzy c-means to get several types of binary images for MR brain image classification. First, we use independent component analysis (ICA) to enhance the components of our original images to reduce the influence of noise. Then we use k-means or fuzzy c-means to get the first classified images. But if the original images have noise, k-means and fuzzy c-means can't deal with it very well. Fisher's linear discriminate analysis (FLDA) which performs classification iteratively to produce final results. The experimental results show the proposed approach has great promise in unsupervised classification.
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