Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/8110
標題: MRI腦部影像圖形使用者介面開發以及參數選擇
The Development of Graphics User Interface for MRI Brain Imaging and The Optimal Parameter Finding
作者: 林盈成
Lin, Ying-Cheng
關鍵字: Independent component analysis (ICA)
獨立成分分析
Support vector machine (SVM)
Graphics User Interface (GUI)
Matlab
支援向量機,
圖形使用者介面
Matlab
出版社: 電機工程學系所
引用: [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
摘要: 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
URI: http://hdl.handle.net/11455/8110
其他識別: U0005-1707200821095900
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-1707200821095900
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