Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/8694
標題: 高頻譜影像分析法對於非監督式腦部磁振造影像組織分類
A Hyperspectral Imaging Approach to Unsupervised Magnetic Resonance Brain Tissue Classification
作者: 陳士煜
Chen, Shih-Yu
關鍵字: Pixel Purity Index;純度像素索引;Magnetic Resonance Imaging;磁振造影像
出版社: 電機工程學系所
引用: Reference [1] G.. A. Wright, “Magnetic resonance imaging,” IEEE Signal Processing Magazine, 14(1): 56-66, 1997 [2] C.-I Chang, Hyperspectral Imaging: Techniques for Spectral Detection and Classification, Kluwer Academic Publishers, 2003. [3] J. 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 [4] 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. [5] J. C. Bezdek, L. O. Hall and L. Clarke, “Review of MRI segmentation techniques using pattern recognition,” Medical Physics, 20: 1033-1048, 1993. [6] A. Hyvarinen, J. Karhunen and E. Oja, Independent Component Analysis, New York: John Wiley & Sons, 2001. [7] 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. [8] 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. [9] 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. [10] 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. [11] 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. [12] 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 [13] 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. [14] 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. [15] F. Chaudhry, C. Wu, W. Liu, C.-I Chang and A. Plaza, Pixel Purity Index-Based Algorithms for Endemember Extraction from Hyperspectral Imagery, Chapter 2, Recent Advances in Hyperspectral Signal and Image Processing, edited by C.-I Chang, Trivandrum, Kerala: Research Signpost, India, 2006. [16] C-I Chang and A. Plaza, “Fast iterative algorithm for implementation of pixel purity index,” IEEE Trans. on Geoscience and Remote Sensing Letters, vol. 3, no. 1, pp. 63-67, January 2006. [17] S. Haykin, Neural Networks: A Comprehensive Foundation, 2nd ed., Prentice-Hall, 1999, Chapter 6. [18] R. O. Duda and P.O. Hart, Pattern Classification and Scene Analysis, New York: John Wiley & Sons, 1973. [19] Boardman, 1992, SIPS User's Guide Spectral Image Processing System, Version 1.2, Center for the Study of Earth from Space, Boulder. [20] F. A., Kruse.; A. B., Lefkoff.; J. W., Boardman; Heiedbrecht; A. T.; P. J. , Barloon; 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. [21] 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. [22] 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. [23] R. C., Gonzalez, R. E., Woods, and Eddins, Digital Image Processing Using MATLAB, Prentice Hall, Upper Saddle River, NJ, 2004; 164-165. [24] http://www.bic.mni.mcgill.ca/brainweb, first date: May, 1997; last modified date: Jun, 2006. [25] S. Theodoridis, K. Koutroumbas. Pattern recognition. 2nd, New York, Academic Press 2003; 411-415. [26] A. Hyvärinen, and E. Oja, Independent Component Analysis: Algorithms and Applications, Neural Networks, 13(4-5):411-430, 2000. [27] A. Hyvärinen, The fixed-point algorithm and maximum likelihood estimation for independent component analysis, Neural Processing Letters, 1999. [28] V. N. Vapnik, Statistical Learning Theory, New York: John Wiley & Sons, 1998. [29] R. O. Duda and P. O. Hart, Pattern Classification and Scene Analysis, New York: John Wiley & Sons, 1973. [30] H. Ren, Q. Du, J. Wang, C.-I Chang and J. Jensen, “Automatic target recognition hyperspectral imagery using high order statistics,” IEEE Trans. on Aerospace and Electronic Systems, vol. 42, no. 4, pp. 1372-1385, Oct. 2006.
摘要: 
高頻譜影像分析法是一種新興的遙測技術並在近期被發現可應用在磁振造影像方面。本論文主要研究純度像素索引(PPI)的新技術研發,純度像素索引被廣泛運用在端點截取的演算法中,本研究將其運用在腦部磁振造影像上,其主要目的是可以在無需任何背景知識下直接挑選訓練樣本並且能對於腦部磁振造影像做出有效的分類。在非監督式的分類法中有兩個重要的議題,第一是如何產生訓練樣本;第二是如何找到適當的分類器進而做有效的分類。為解決第一項議題,我們運用了純度像素索引來擷取一組適合的訓練樣本。再者,我們使用支援向量器(SVM)以及費雪線性辨別分析(FLDA)來解決第二項議題,本實驗結果顯示本論文所提出的演算法能在非監督式的分類法中,對於腦部磁振造影像做出有效的分類。

Hyperspectral imaging approach is an emerging remote sensing technique which has recently found applications in magnetic resonance imaging. In this thesis, we develop a new application of using a widely used endmember extraction algorithm, called pixel purity index (PPI) to find training samples directly from the data without prior knowledge for MR brain image classification. Two major challenging issues arise in unsupervised classification. One is how to generate desired knowledge directly from the data in an unsupervised manner. The other is how to find an appropriate follow-up classifier to use the obtained unsupervised knowledge to perform supervised classification. This study presents a new approach to unsupervised classification for magnetic resonance imaging. To address the first issue the pixel purity index (PPI) which is commonly used in hyperspeftral imaging for endmember extraction is used to find a good set of initial training samples without prior knowledge. To address the second issue the PPI-found samples are then used as training samples for a support vector machine to find a good set of training samples for a follow-up supervised classifier, 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.
URI: http://hdl.handle.net/11455/8694
其他識別: U0005-2801201014470600
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