Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/8110
DC FieldValueLanguage
dc.contributor馬代駿zh_TW
dc.contributor.advisor歐陽彥杰zh_TW
dc.contributor.author林盈成zh_TW
dc.contributor.authorLin, Ying-Chengen_US
dc.contributor.other中興大學zh_TW
dc.date2009zh_TW
dc.date.accessioned2014-06-06T06:41:01Z-
dc.date.available2014-06-06T06:41:01Z-
dc.identifierU0005-1707200821095900zh_TW
dc.identifier.citation[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.htmlzh_TW
dc.identifier.urihttp://hdl.handle.net/11455/8110-
dc.description.abstract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。zh_TW
dc.description.abstractUsing 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 parameteren_US
dc.description.tableofcontentsCHAPTER 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 72en_US
dc.language.isoen_USzh_TW
dc.publisher電機工程學系所zh_TW
dc.relation.urihttp://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-1707200821095900en_US
dc.subjectIndependent component analysis (ICA)en_US
dc.subject獨立成分分析zh_TW
dc.subjectSupport vector machine (SVM)en_US
dc.subjectGraphics User Interface (GUI)en_US
dc.subjectMatlaben_US
dc.subject支援向量機,zh_TW
dc.subject圖形使用者介面zh_TW
dc.subjectMatlabzh_TW
dc.titleMRI腦部影像圖形使用者介面開發以及參數選擇zh_TW
dc.titleThe Development of Graphics User Interface for MRI Brain Imaging and The Optimal Parameter Findingen_US
dc.typeThesis and Dissertationzh_TW
item.languageiso639-1en_US-
item.openairetypeThesis and Dissertation-
item.cerifentitytypePublications-
item.grantfulltextnone-
item.fulltextno fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
Appears in Collections:電機工程學系所
Show simple item record
 
TAIR Related Article

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.