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Multispectral Brain Magnetic Resonance Image Analysis Using Weighted Radial Basis Function Kernels for Support Vector Machine
|關鍵字:||Weighted-RBF Kernels;權重式高斯核心;SVM;支援向量機||出版社:||通訊工程研究所||引用:|| G.. A. Wright, “Magnetic resonance imaging,” IEEE Signal Processing Magazine, 14(1): 56-66, 1997  C.-I Chang, Hyperspectral Imaging: Techniques for Spectral Detection and Classification, Kluwer Academic Publishers, 2003.  J. W. Boardman, F. A. Kruse and R. O. Green, “Mapping target signatures via partial unmixing of AVIRIS data,” Summaries of JPL Airborne Earth Science Workshop, Pasadena, CA, 1995  L. Clarke, R. Vethuizen, M. Camacho, J. Heine, M. Vaidyanathan, L.O. Hall, R.W. Thatcher and M.L. Silbiger, “MRI segmentation: methods and applications,” Magnetic Resonance Imaging, 13(3): 343-368, 1995.  J. C. Bezdek, L. O. Hall and L. Clarke, “Review of MRI segmentation techniques using pattern recognition,” Medical Physics, 20: 1033-1048, 1993.  A. Hyvarinen, J. Karhunen and E. Oja, Independent Component Analysis, New York: John Wiley & Sons, 2001.  T. Nakai, S. Muraki, E. Bagarinao, Y. Miki, Y. Takehara, K. Matsuo, C. 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本論文主要將支援向量機(SVM)的高斯核心(RBF kernel)衍伸為權重式高斯核心(weighted-RBF kernel)。一般我們使用0.5的自相關矩陣作為RBF kernel 的係數，本研究將探討依影像本身之相關矩陣(correlation matrix)、共變異數矩陣(covariance matrix)及內變異數矩陣(within-class covariance matrix)運用在RBF kernel上，並以不同比例隨機選取訓練樣本點。其主要目的是可以依據影像本身之統計特性，選取適合的RBF kernel，對腦部磁振造影影像做出有效的分類。在SVM中有兩個重要的議題，第一是如何選擇kernel；第二是如何決定訓練樣本的數量。為解決第一項議題，我們使用了weighted-RBF kernel。第二項議題，我們以不同百分比數量的樣本來探討。最後，我們使用支援向量器(SVM)以及費雪線性辨別分析(FLDA)來提高分類效果。實驗結果顯示本論文所提出的演算法，對於腦部磁振造影影像做出有效的分類。
In this thesis, we will extend the RBF kernel of support vector machine (SVM) to weighted RBF kernel. The default coefficient of RBF kernel is an identity matrix with 0.5. This study will explore the correlation matrix, the covariance matrix and the covariance matrix by image itself on the use of the RBF kernel, and with different training samples which were randomly selected. Its main purpose is that we can select an appropriate RBF kernel who base on the statistical properties of image itself. Two major challenging issues arise in SVM: one is how to choice a proper kernel; the other is how to decide the quantity about training samples. To address the first issue, we propose the W-RBF kernel. To address the second issue, we discuss different number of percentage of samples. Finally, we combine the support vector control (SVM) and Fisher linear discriminant analysis (FLDA) to improve the classification results. Experimental results show that that the proposed algorithm has great promise in the brain magnetic resonance image.
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