Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/4909
標題: 非監督式多發性硬化症偵測和分類方法在腦部核磁共振成像
Unsupervised MS Lesion Detection And Classification Method on Brain MRI Images
作者: 張智凱
Chang, Chih-Kai
關鍵字: 自動目標物產生過程;Automatic target generation process;純度像素索引;限制能量最小化法;正交子空間投影法;pure pixel index;constrained energy minimization;orthogonal subspace projection
出版社: 通訊工程研究所
引用: [1] T. B, Dyrby, E. Rostrup, W. Baare, et. al. Segmentation of age-related white matter changes in a clinical multi-center study. NeuroImage, 41: 335–345, 2008. [2] http://www.wisegeek.org/what-are-white-matter-lesions.htm [3] L. T. Westlye, K.B.Walhovd, A.M. Dale, et. al. Increased sensitivity to effects of normal aging and Alzheimer''s disease on cortical thickness by adjustment for local variability in gray/white contrast: A multi-sample MRI study. NeuroImage, 47: 1545–1557, 2009. [4] G. A. Wright, “Magnetic resonance image,” IEEE Signal Processing Mag., pp. 56–66, Jan. 1997. [5] G. Sebastiani and P. Barone, “Mathematical principles of basic magnetic resonance image in medicine,” Signal Process, vol. 25, pp. 227–250, 1991. [6] D. S. David, and G. B. William, Magnetic Resonance Imaging, vol. 1, 3rd, Mosby Inc.,1999. [7] C. I. Chang, Hyperspectral Imaging: Techniques for Spectral Detection and Classification, Kluwer Academic Publishers, 2003. [8] 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 [9] 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. [10] 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. [11] C. I. Chang,“Automatic Spectral Target Recognition in Hyperspectral Imagery, ”Manuscript received August 10, 2001; revised December 10, 2002; released for publication October 4, 2003. [12] M. Hearst, ed., “Support Vector Machines,” IEEE Intelligent Systems Magazine, Trends and Controversies, Marti Hearst, ed., vol 13, no 4, (1998). [13] S. Haykin, Neural Networks: A Comprehensive Foundation, 2nd ed., Prentice-Hall, 1999, Chapter 6. [14] R. O. Duda and P. O. Hart, Pattern Classification and Scene Analysis, New York: John Wiley & Sons, 1973. [15] Y. C. Ouyang , H. M. Chen, J. W. Chai, C. C. Chen, S. K. Poon, C. W. Yang, S. K. Lee, C. I. Chang: Band Expansion-Based Over-Complete Independent Component Analysis for Multispectral Processing of Magnetic Resonance Image. IEEE Trans. Biomed. Eng. 55, 1666–1677 (2008) [16] http://www.bic.mni.mcgill.ca/brainweb/ [17] S. Theodoridis and K. Koutroumbas, Pattern Recognition,Academic Press, 1999, p. 366 [18] Boardman, 1992, SIPS User’s Guide Spectral Image Processing System, Version 1.2, Center for the Study of Earth from Space, Boulder. [19] S. Theodoridis and K. Koutroumbas, Pattern Recognition,Academic Press, 1999, p. 366 [20] Duda and Hart, Pattern Classification and Scene Analysis, 1973. [21] C. I. Chang, “Multiple-parameter receiver operating characteristic analysis for signal detection and classification,” IEEE Sensors Journal, vol. 10, no. 3, pp.423-442, March 2010. [22] http://www.fil.ion.ucl.ac.uk/spm/software/SPM8/ [23] S. M. Smith, “Fast Robust Automated Brain Extraction,” Human Brain Mapping, vol. 17, pp. 143–155, 2002. [24] Y. Zhang, M. Brady, and S. Smith.“Segmentation of Brain MR Images Through a Hidden Markov Random Field Model and the Expectation-Maximization Algorithm,” IEEE Trans. Medical Imaging, vol. 20, pp. 45-57, 2001. [25] S. M. Smith, M. Jenkinson, M. W. Woolrich, C. F. Beckmann, T. E.J. Behrens, H. Johansen-Berg, P. R. Bannister, M. D Luca, I. Drobnjak, D .E. Flitney, R. K. Niazy, J. Saunders, J. Vickers, Y. Zhang, N.D. Stefano, J.M. Brady, and P.M. Matthews. “Advances in functional and structural MR image analysis and implementation as FSL,” NeuroImage, vol. 23: S208–S219, 2004. [26] http://www.fmrib.ox.ac.uk/fsl/ [27] E. Wong, Partial Volume Estimation of Magnetic Resonance Image Using Linear Spectral Mixing Analysis, Ph.D. dissertation, Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, May 2010. [28] Boardman, 1992, SIPS User’s Guide Spectral Image Processing System, Version 1.2, Center for the Study of Earth from Space, Boulder. [29] 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. [30] J. C. Harsanyi, “Detection and classification of subpixel spectral signatures in hyperspectral image sequences,” Ph.D. dissertation, Dept, Elect. Eng., Univ.Maryland Baltimore County, Baltimore, MD, 1993. [31] J. C. Harsanyi,W. Farrand, and C. I. Chang, “Detection of subpixel spectral signatures in hyperspectral image sequences,” in Proc. American Society of Photogrammetry&Remote Sensing Annu. Meeting, Reno, 1994, pp. 236–247. [32] R. S. Resmini, M. E. Kappus, W. S. Aldrich, J. C. Harsanyi, and M. Anderson, “Mineral mapping with Hyperspectral Digital Imagery Collection Experiment (HYDICE) sensor data at Cuprite, Nevada, U.S.A.,” Int. J. Remote Sensing, vol. 18, no. 17, pp. 1553–1570, 1997. [33] W. Farrand and J. C. Harsanyi, “Mapping the distribution of mine tailing in the Coeur d’Alene river valley, Idaho, through the use of constrained energy minimization technique,” Remote Sensing of Environment, vol. 59, pp. 64–76, 1997. [34] J. C. Harsanyi, and C. I. Chang (1994) Hyperspectral image classification and dimensionality reduction: An orthogonal subspace projection. IEEE Transactions on Geoscience and Remote Sensing, 32 (1994), 779–785. [35] 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.
摘要: 
分類一般而言需要一系列的訓練點,分為監督式或是非監督式的方法取決於從資料中訓練點的產生方式是預先得知或是沒有預先得知資訊。非監督式法在沒有任何預先資訊的情況下來幫助分類的程序比起監督式法更具有挑戰性。為了達成分割需要一系列訓練點來進行分類,在本論文中我們使用了兩種非監督式的演算法,自動目標產生過程和純度像素索引來找尋訓練點。然後應用了限制能量最小化法將多發性硬化症從腦部核磁共振影像中分類出來,限制能量最小化法在沒有得知資料的模型和背景雜訊統計下只需要最少的子樣本數便可以有很好的表現,接著使用正交子空間投影法來做其餘的組織分類。實驗結果顯示本方法在腦部核磁共振影像中有很好的效果。

The classification generally requires a set of training samples, which can be carried out in a supervised or an unsupervised manner depending upon how training samples are produced a priori using prior knowledge or a posteriori obtained directly from the data. Unsupervised methods which do not assume any prior scene knowledge can be learned to help classification process are obviously more challenging than the supervised ones. In order for segmentation to perform classification, a set of training samples is required. In this thesis we present two unsupervised target detection methods, automatic target generation process (ATGP) and pure pixel index (PPI), to find training samples. Then we apply constrained energy minimization (CEM) method to classify multiple sclerosis (MS) lesion for MR brain image. This CEM method is perfectly used due to the fact that it requires the least amount of information about subsample target of interest without making assumptions on signal model and noise/background statistics. After that the orthogonal subspace projection (OSP) method is also applied to classify the rest of tissues. Experimental results show that these approaches have great promise in MR brain image classification.
URI: http://hdl.handle.net/11455/4909
其他識別: U0005-0508201316264000
Appears in Collections:通訊工程研究所

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