Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/8391
標題: 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
Improving Brain Tissue Classification ICA+SVM of MRI acquired with multiple-channel phase-array coil
作者: 李宜修
Lee, Yi-Hsiu
關鍵字: Inhomogeneity;非均一性;Independent component analysis (ICA);Support vector machine (SVM);獨立成分分析;支援向量機
出版社: 電機工程學系所
引用: [1] www.mr-tip.com/serv1.php?type=db1&dbs=Field%20Inhomogeneity%20Artifact [2] Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P SENSE: sensitivity encoding for fast MRI. (1999): [3] Fa-Hsuan Lin 1 2 3 *, Ying-Jui Chen 4, John W. Belliveau 2 3, Lawrence L. Wald 2 3.A wavelet-based approximation of surface coil sensitivity profiles for correction of image intensity inhomogeneity and parallel imaging reconstruction, Human Brain Mapping 19:96-111(2003) [4] Daubechies : Ten lectures on wavelets, Society for Industrial and Applied Mathematics, Philadelphia, PA. (1992): [5] J. G. Sled and G. B. Pike, “Standing-wave and RF penetration artifacts caused by elliptic geometry: an electrodynamic analysis of MRI,” IEEE Trans. Med. Imag., vol. 17, no. 4, pp. 653-662, Aug. (1998). [6] A. Simmons, P. S. Tofts, G. J. Barker, and S. R. Arridge, “Sources of intensity nonuniformity in spin-echo images at 1.5T,” Magn. Reson. Med., vol. 32, no. 1, pp. 121-128, (1994). [7] Olivier Salvado, Claudia Hillenbrand, Shaoxiang Zhang, and David L. Wilson*, Member, IEEE, Method to Correct Intensity Inhomogeneity in MR Images for Atherosclerosis Characterization, ieee transactions on medicalimaging, vol. 25, no. 5, May (2006) [8] P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 12, no. 7, pp. 629-639, Jul. (1990) [9] M. J. Black, G. Sapiro, D. H. Marimont, and D. Heeger, “Robust anisotropic diffusion,” IEEE Trans. Image Process., vol. 7, no. 3, pp. 421-432, Mar. 1998. [10] O. Salvado, C.Hillenbrand, S. Zhang, J. S. Suri, and D. L.Wilson, “MR signal inhomogeneity correction for visual and computerized atherosclerosis lesion assessment,” Proceeding of 2004 IEEE International Symposium on Biomedical Imaging pp. 1143-1146,(2004). [11] mathworld.wolfram.com/CubicSpline.html [12] Axel, L., Constantini, J., Listerud, J.,. Intensity correction in surfacecoil MR imaging. Am. J. Roentgenol. 148, 418-420.(1987) [13] Dawant, B., Zijdenbos, A., Margolin, R.,. Correction of intensity variations inMR images for computer-aided tissue classification. IEEE Trans. Med. Imaging 12 (4), 770-781.( 1993) [14] Meyer, C., Bland, P., Pipe, J., Retrospective correction of intensity inhomogeneities in MRI. IEEE Trans. Med. Imaging 14 (1), 36-41.(1995) [15] Wells III, W., Grimson, W., Kikinis, R., Jolesz, F., Adaptative segmentation of MRI data. IEEE Trans. Med. Imaging 15 (4), 429-442 (1996) [16] Shannon, C., A mathematical theory of communication. Bell Syst. Tech. J. 27, 623-656.(1948) [17] Likar, B., Maintz, J., Viergever, M., Pernus, F.,. Retrospective Shading correction based on entropy minimization. J.Microsc. 197 (3), 285-295.(2000) [18] Likar, B., Viergever,M., Pernus, F., Retrospective correction of MR intensity inhomogeneity by information minimization. IEEE Trans. Med. Imaging 20 (12), 1398-1410. (2001) [19] Press, W., Flannery, B., Teukolsky, S., Vetterling, W.,. Numerical Recipes in C: The Art of Scientific Computing, second ed. Cambridge University Press, UK. (2002) [20] Otsu, N.,. A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybernet. 9 (1), 62-69.(1979) [21] Jose ´ V. Manjo ´n a, *, Juan J. Lulla, Jose ´ Carbonell-Caballero a, Gracia ´n Garcı ´a-Martı ´a Luı ´s Martı ´-Bonmatı ´b, Montserrat Robles, A nonparametric MRI inhomogeneity correction method, Medical Image Analysis 11 (2007) 336-345 [22] Olivier Salvado, Claudia Hillenbrand, Shaoxiang Zhang, and David L. Wilson*, Member, IEEE, Method to Correct Intensity Inhomogeneity in MR Images for Atherosclerosis Characterization, IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 25, NO. 5, (2006) [23] Fa-Hsuan Lin, * Ying-Jui Chen, John W. Belliveau, and Lawrence L. Wald, A Wavelet-Based Approximation of Surface Coil Sensitivity Profiles for Correction of Image Intensity Inhomogeneity and Parallel Imaging Reconstruction, Human Brain Mapping 19:96-111(2003)
摘要: 
非均一性在MRI中是一個重要的現象,它會影響MRI的應用,使其在醫學上的研究因此受影響。而現在MR儀器中多使用多通道線圈,因為訊號的干擾使其非均一性現象將更加嚴重。在另一方面分類方法獨立成分分析+支援向量機也受到非均一性的因素,造成分類效果受到影響。所以本研究將針獨立成分分析+支援向量機搜尋最適合的非均一性修正方法,進而改善期分類效果。我們選取了三種修正非均一性的方法DWT、LEM-CS以及LEM-BS。而LEM-BS是所有這些方法中最適合ICA+SVM分類器的,此外我們將LEM-BS做修改,將其濾波器置換為Gaussian濾波器,進而獲得更好的結果。另外LEM-BS不單只執行單張影像,更可一次執行多張修正非均一性,其多張修正效果不亞於單張修正,有時更勝。最後LEM-BS無論在高解析度或低解析都有不錯的修正效果。

Since the multi-channel coil of magnetic resonance imaging (MRI) became a mainstream, methods to reduce inhomogeneity in MRI without using the body coil for additional scan have developed. The interference of signals made the appearance of MR image badly. As a result it serious affects the classifications for MR images. In this study we would find the most adaptable method to improve inhomogeneity for MRI and get improvement for brain tissue classification by using independent component analysis (ICA) and support vector machine (SVM) method under multiple-channel phase-array coil. We chose three inhomogeneity correction methods: discrete wavelet transform (DWT), local entropy minimize with B-spline (LEM-BS) and local entropy minimize with cubic spline (LEM-CS). In our experiment, these three methods were used as the pre-processing method before applying ICA + SVM to correct the inhomogeneity of MR image. Web site images are first applied in the experiment and the Tanimoto index was used to measure the performance for the three methods. Real phantom MR images were also applied in this experiment. The results show that the LEM-BS is the best choice. Instead of using the average filter for LEM-BS, we use Gaussian filter to get better classification result. The LEM-BS method can be applied not only in single slice but also in multiple slices and sometimes it shows better result.
URI: http://hdl.handle.net/11455/8391
其他識別: U0005-2907200816295800
Appears in Collections:電機工程學系所

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