# 臺灣博碩士論文加值系統

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 在結合獨立成份分析(ICA)與支援向量機(SVM)於核磁共振影像(MRI)上，我們對分類的結果有不錯的表現。我們為了更進一步加速收集MR影像以統計不同疾病造成的腦體積容量的改變，我們使用Matlab 來發展具實用性的圖型使用者軟體介面來幫助臨床醫學的研究，我們所發展的程式也包含了一些影像處裡工具，和一些輔助性的演算法，例如分水嶺演算法等等，再日後希望進一步增加有用的處理工具，以方便今後的研究與臨床醫學有關的用途。這篇論文也探討SVM參數部份，我們知道參數的選擇對支援向量機的結果有很大的影響, 我們將會研究比較參數在ICA加上SVM和SVM的結果，最後我們會得知使用ICA做前處理後，假如SVM參數在一個合理範圍值，我們可以不用在意SVM參數的選擇，而可以得到不錯的分類結果。
 Using independent component analysis (ICA), combining with support vector machines (SVM), in magnetic resonance imaging (MRI) analysis shows good performance. Different disease can have different effect on the brain volume change which can be detected by carefully exam the brain MR image. In order to increase the collection speed we have developed an useful GUI software for clinical research purpose. We also carefully examined the parameters of SVM, The parameters has great influence on the results of SVM. We have studied and compared the result of ICA plus SVM and SVM only. We can conclude that the MR image pass through the ICA process then SVM for classification could lead better result without worry about the selection for SVM parameter
 CHAPTER 1 Introduction 1CHAPTER 2 BACKGROUND 42.1 Independent Component Analysis 4 2.1.1 Introduction 4 2.1.2 Definition 62.2 Support Vector Machine (SVM) 9 2.2.1 Linearly Separable Patterns for SVM 9 2.2.2 Linearly Non-Separable Patterns fro SVM 13 2.2.3 Non-linearly Separable Patterns for SVM 162.3 Watershed Transform 18 2.3.1 Introduction 18 2.3.2 Definition 192.4 Quantitative measurement 24 2.4.1 Multi-parametric Analysis 24 2.4.2 Similarity Measures 25CHAPTER 3 Graphics User Interface 263.1 Introductions 263.2 Design Components 263.3 Windows Interface Design 283.4 Implementation 293.5 CLI and GUI Comparison 303.6 Example 32 3.6.1 Using SVM Process 32 3.6.2 Using ICA plus SVM Example 37 3.6.3 Using Watershed Mask to Strip-Skull 38 3.6.4 Using User Manual Mask to Strip-Skull 423.7 Other Tool 46 3.7.1 GUI Design for ICA and SVM Parameter Selection 46 3.7.2 GUI Design for Best SVM Parameter Finding 47 3.7.3 Other Assistant Tools 48CHAPTER 4 Parameter Selection for MRI Image Using ICA and SVM 524.1 Introduction 524.2 SVM Kernel Function Parameter Selection 52 4.2.1 Use RBF kernel 53 4.2.2 Optimal Parameter Finding 53 4.2.3 Cross-Validation and Grid-Search 544.3 Statistics Test 57 4.3.1 Pair T-test 57 4.3.2 ANOVA 584.4 Design Experiment 60 4.4.1 Introduction for Web Brain Image 60 4.4.2 Experiment Step 624.5 Best parameter in SVM 644.6 Parameter in ICA plus SVM and SVM 674.7 More Training Samples in ICA plus SVM and SVM 69Chapter 5 Conclusion 71Reference 72
 [1] A. Hyvärinen, J. Karhunen and E. Oja, Independent Component Analysis, John Wiley & Sons, 2001.[2] T. Nakai, S. Muraki, E. Bagarinao, Y. Miki, Y. Takehara, K. Matsuo, C. Kato, H. Sakahara, Isoda, Application of independent component analysis to magnetic resonance imaging for enhancing the contrast of gray and white matter, NeuroImage, vol. 21, pp. 251-260, 2004.[3] A.J. Bell and T.J. Sejnowski, An information-maximization approach to blind separation and blind deconvolution. Neural Computation, vol. 7, pp. 1129-1159, 1995.[4] A. Hyvärinen and E. Oja, A fast fixed-point Algorithm for independent component analysis, Neural Computation, vol. 9, no. 7, pp. 1483-1492, 1997.[5] A. Papoulis, Probability, Random Variables, and Stochastic Processes, McGraw-Hill, 3rd edition, 1991.[6]. A. Hyvärinen, and E. Oja, Independent Component Analysis: Algorithms and Applications, Neural Networks, 13(4-5):411-430, 2000.[7] A. Hyvärinen, The fixed-point algorithm and maximum likelihood estimation for independent component analysis, Neural Processing Letters, 1999. To appear.[8] A. Hyvärinen, Survey on Independent Component Analysis, Neural Computing Surveys 2, 94-128, 1999.[9] S. Haykin, Neural Networks: A Comprehensive Foundation, 2nd ed., Prentice-Hall, 1999, Chapter 6.[10] V.N. Vapnik, Statistical Learning Theory, John Wiley & Sons, 1998.[11] H. Digabel and C. Lantuejoul, Iterative algorithms, Actes du Second Symposium Europeen d''Analyse Quantitative des Microstructures en Sciences des Materiaux, Biologie et Medecine, Caen, 4-7 October 1977, J.-L. Chermant, Ed., Riederer Verlag, Stuttgart, pp. 85-99, 1978.[12] C. Lantuejoul, La squelettisation et son application aux mesures topologiques des mosaiques polycristallines. PhD thesis, Ecole des Mines, Paris, 1978.[13] 徐賢鈞,自適影像分割技術及三維重建-以腦部磁振造影解剖影像之大腦組織結構分割為案例,大葉大學工業工程學系碩士論文,民國九十三年六月。[14] H.K. Hahn and H.O. Peitgen, The skull striping problem in MRI solved by a single 3D watershed transform, Proc. MICCAL, LNCS 1935, pp.134-143, 2000.[15] P. Tofts Quantitative MRI of the Brain: Measuring Changes Caused by Disease, John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England.[16] S. Theodoridis and K. Koutroumbas. Pattern Recognition, 2nd ed, Elsevier Science.[17] Hsu, Chih-Wei, Chang, Chih-Chung, and Lin,Chih-Jen, “A Practical Guide to Support VectorClassification,” Available at http://www.csie.ntu.edu.tw/~cjlin/papers/guide/ guide.pdf, 2003.[18] Keerthi, S. S. and C.-J Lin (2003). Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Computation 15(7), 1667-1689.[19] Lin, H.-T. and C.-J. Lin (2003). A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods. Technical report, Department of Computer Science, National Taiwan University[20] DTREG, http://www.dtreg.com/svm.htm[21] 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, 2007 (Accepted)[23] Matlab, http://www.mathworks.com/[24] http://www.bic.mni.mcgill.ca/brainweb/faq.html
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 1 自適式影像分割技術及三維重建—以腦部磁振造影解剖影像之大腦組織結構分割為案例 2 整合ICA與SVM於多變量製程失控品質特性辨識之研究 3 整合獨立成分分析與支援向量機建立管制圖非隨機樣式之辨識系統 4 有效運用ICA與分類器於腦部MR影像顱內組織量化測量之探討 5 利用ICA及分類器作MRI影像分析 6 應用獨立成分分析與支援向量機偵測與辨識產品件內和件間之變異 7 改善多通道相位陣列線圈之腦部磁振造影像組織分類ICA+SVM績效之研究 8 獨立成份分析演算法在多頻譜腦部磁振造影像分析之應用

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 1 有效運用ICA與分類器於腦部MR影像顱內組織量化測量之探討 2 利用ICA及分類器作MRI影像分析 3 改善多通道相位陣列線圈之腦部磁振造影像組織分類ICA+SVM績效之研究 4 由MRI影像辨識膝關節軟骨與誤差分析 5 獨立成份分析演算法在多頻譜腦部磁振造影像分析之應用 6 快速自動標記CT與MRI腦部影像之AC與PC組織 7 自適式影像分割技術及三維重建—以腦部磁振造影解剖影像之大腦組織結構分割為案例 8 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 9 評估0.2T磁振造影影像之幾何形變 10 應用MATLAB計算洪流演算以翡翠水庫為例 11 基於模糊邏輯之多頻譜MRI影像切割與分類研究 12 基於腦波頻譜變化探討聆聽音樂之情緒反應 13 以MRI為基礎之聽神經瘤切割系統 14 運用PET顏色校正與權重式MRI灰階強度補強演算法之腦部醫學影像融合系統 15 應用灰關聯分析於肝功能評估因子權重之分析

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