Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/9147
標題: 使用限制能量最小化法偵測MRI影像的腦部病變部位
Brain Lesions Detection Using Constrained Energy Minimization for MRI
作者: 黃健揚
Huang, Jian-Yang
關鍵字: MRI;MRI;限制能量最小化 (Constrained Energy Minimization, CEM);腦部病變偵測;Constrained Energy Minimization;CEM;brain lesion detection
出版社: 電機工程學系所
引用: [1]Dyrby TB, Rostrup E, Baare W, et. al. Segmentation of age-related white matter changes in a clinical multi-center study. NeuroImage, 41: 335–345, 2008. [2]Westlye LT, Walhovd KB, Dale AM, 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. [3]http://www.wisegeek.org/what-are-white-matter-lesions.htm [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]David D. Stark, and William G. Bradley., Magnetic Resonance Imaging, vol. 1, 3rd, Mosby Inc.,1999. [7]Andersen AH, Zhang Z, Avison MJ, Gash DM. Automated segmentation of multispectral brain MR images. J Neurosci Methods 2002; 122:13-23. [8]Clarke LP, Velthuizen RP, Camacho MA, et al. MRI segmentation: methods and applications. Magn Reson Imaging 1995; 13: 343-368. [9]Soltanian-Zadeh H, Peck DJ, Windham JP, Mikkelsen T. Brain tumor segmentation and characterization by pattern analysis of multispectral NMR images. NMR Biomed 1998; 11: 201-208. [10]Mohamed FB, Vinitski S, Faro SH, Gonzalez CF, Mack J, Iwanaga T. Optimization of tissue segmentation of brain MR images based on multispectral 3D feature maps.Magn Reson Imaging 1999; 17: 403-409. [11]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. [12]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. [13]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. [14]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. [15]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 [16]http://www.bic.mni.mcgill.ca/brainweb/faq.html [17]S. Theodoridis and K. Koutroumbas. Pattern Recognition, 2nd ed, Elsevier Science. [18]C. I. Chang (張建禕), E. Wong “TWO-DIMENSIONAL TANIMOTO INDEX FOR CONTINUOUS DECISION MADE IMAGE CLASSIFICATION, conference on Computer Vision, Graphics, and Image Processing, 2012 [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]www.bic.mni.mcgill.ca/ brainweb/ [23]http://www.fil.ion.ucl.ac.uk/spm/software/SPM8/ [24]S. M. Smith, “Fast Robust Automated Brain Extraction,” Human Brain Mapping, vol. 17, pp. 143–155, 2002. [25]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. [26]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. [27]http://www.fmrib.ox.ac.uk/fsl/ [28]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.
摘要: 
過去研究顯示[1-2],許多腦部病變和退化性所引起之疾病,隨著疾病之不同,受到影響腦部區域之灰、白質改變亦不相同。從臨床神經症狀之表現來評估衡量病患,亦各異其趣。就醫學影像檢查角度而言,由於腦部灰、白質組織特性相近不易有效區隔,因此如何在3維醫學影像架構下將腦部灰、白質病變組織做有效、準確的分割,是當下研究腦部病變上一項重要課題。
本篇論文提出一種方法,以限制能量最小化法來突顯白質病變的分布位置。由於突顯後的影像為灰階影像,不是二值化影像,所以在合成影像的部分以2D Tanimoto的方法找出其最佳的Threshold;而在實際影像的部分則是預先設定一個Threshold對結果影像做二值化的處理。最後再比較於一般情況下以及執行過維度擴充後的偵測效率,結果發現,無論是在合成影像上的不同雜訊等級中的偵測狀況或者是在實際影像上的偵測表現,執行過維度擴充之後均能大幅提升其偵測效率。

One of the fundamental tasks of MRI is tissue classification. White matter lesions are small areas of dead cells found in parts of the brain that act as connectors. This thesis presents a method that uses constrained energy minimization (CEM) approach to highlight the location of white-matter lesions. The highlighted image is the gray level image rather than binary image therefore we use a 2D Tanimoto index to find the optimal threshold in the brain web image. A threshold should be selected prior to be used in the real image. Experimental results demonstrate that the CEM significantly improve over RBF kernels in SVM-classification. Most importantly, CEM should also joint with band expansion process (BEP) to offers a great increase in detection performance.
URI: http://hdl.handle.net/11455/9147
其他識別: U0005-0608201316495600
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