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研究生:林盈成
研究生(外文):Ying-Cheng Lin
論文名稱:MRI腦部影像圖形使用者介面開發以及參數選擇
論文名稱(外文):The Development of Graphics User Interface for MRI Brain Imaging and The Optimal Parameter Finding
指導教授:歐陽彥杰
學位類別:碩士
校院名稱:國立中興大學
系所名稱:電機工程學系所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:中文
論文頁數:73
中文關鍵詞:獨立成分分析支援向量機圖形使用者介面Matlab
外文關鍵詞:Independent component analysis (ICA)Support vector machine (SVM)Graphics User Interface (GUI)Matlab
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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 1
CHAPTER 2 BACKGROUND 4
2.1 Independent Component Analysis 4
2.1.1 Introduction 4
2.1.2 Definition 6
2.2 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 16
2.3 Watershed Transform 18
2.3.1 Introduction 18
2.3.2 Definition 19
2.4 Quantitative measurement 24
2.4.1 Multi-parametric Analysis 24
2.4.2 Similarity Measures 25
CHAPTER 3 Graphics User Interface 26
3.1 Introductions 26
3.2 Design Components 26
3.3 Windows Interface Design 28
3.4 Implementation 29
3.5 CLI and GUI Comparison 30
3.6 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 42
3.7 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 48
CHAPTER 4 Parameter Selection for MRI Image Using ICA and SVM 52
4.1 Introduction 52
4.2 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 54
4.3 Statistics Test 57
4.3.1 Pair T-test 57
4.3.2 ANOVA 58
4.4 Design Experiment 60
4.4.1 Introduction for Web Brain Image 60
4.4.2 Experiment Step 62
4.5 Best parameter in SVM 64
4.6 Parameter in ICA plus SVM and SVM 67
4.7 More Training Samples in ICA plus SVM and SVM 69
Chapter 5 Conclusion 71
Reference 72
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[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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