Please use this identifier to cite or link to this item:
標題: MR腦部灰質影像自動化分割之等位函數法
The Automatic Segmentation of Gray Matter of the Brain MR Images with Level-Set Method
作者: 林曉郁
Lin, Xiao-Yu
關鍵字: Level Set Method;等位函數法;Fuzzy C-Means Clustering;Image Segmentation;Magnetic Resonance Image;Surface Rendering;模糊分群;影像分割;腦部MR影像;表面成像演算法
出版社: 應用數學系所
引用: [1] M. Kass, A. Witkin, and D. Terzopoulos, “Snakes - Active Contour Models”. International Journal of Computer Vision, 1(4): 321-331, 1987. [2] F. Leymarie and M. D. Levine. “Tracking deformable objects in the plane using an active contour model”. IEEE Trans. on Pattern Anal. Machine Intell., 15(6):617-634, 1993. [3] C. Xu & al.” Reconstruction of the Human Cortical Surface from MR Images”. IEEE transactions on Medical Imaging, Vol 18, No 6, June 1999 [4] D.W. Shattuck and R.M. Leahy. BrainSuite,” An Automated Cortical Surface Identification Tool” [invited article] Medical Image Analysis (in press). [5] Tony F. Chan, Member, IEEE, and Luminita A. Vese”Active Contours Without Edges”, Image Processing, IEEE Transactions on, Volume:10, Issue:2, Feb. 2001, pp.266-277 [6] Xiao Han; Chenyang Xu; Prince, J.L,”A topology preserbing level set method for geometric deformable models”, Pattern Analysis and Machine Intelligence, IEEE Transactions on, Volume:25, Issue:6, June [7] B. Wandell, S. Chial and B. Backus (2000). “Visualization and Measurement of the Cortical Surface”. Journal of Cognitive Neuroscience, vol. 12, no. 5. pp. 739-52 [8] [9] Teo, P.C., Sapiro, G. and Wandell, B.A. (1997). “Creating connected representations of cortical gray matter for functional MRI visualization”. IEEE Transactions on Medical Imaging 16: 852-863 [10] T. Kapur, W. E. L. Grimson, R. Kikinis. “Segmentation of Brain Tissue from MR image”. AITR-1566, June 95. [11] D.W. Shattuck, S.R. Sandor-Leahy, K.A. Shaper, D.A. Rottenberg, R.M. Leahy. “Magnetic resonance image tissue classification using a partial volume model”. Neuroimage 2001; 13(5): 856-876 [12] F. Segonne, A.M. Dale, E. Busa, M. Glessner, D. Salat, H.k. Hahn, B.Fischi, , “A hybrid approach to the Skull Stripping problem in MRI”. Human Brain Map., Brighton UK, (2001) [13] Wayne, Lin Wei-Cheng,” Mathematical Morphology and Its Applications on Image Segmentation”, Dept. of Computer Science and Information Engineering, National Taiwan University, June 7, 2000 [14] Rafael C. Gonzalez, Richard E. Woods,” Digital Image Processing”. [15] Laurent D.Cohen,Isaac Cohen, “Finite-Element Methods for 2-D and 3-D Images”, 1993, IEEE [16] Laurent D.Cohen , “Deformadle Surgaces and Parametric Models to Fit and Track 3D Data” , Systems, Man, and Cybernetics, IEEE International Conference on , Volume:4 , 14-17 Oct. 1996 pp.2451-2456 vol. 4 [17] Chenyang Xu and Jerry L. Prince ”Gradient Vector Flow:A New External Force for Snakes”, IEEE Computer Society Conference on , 17-19 June 1997, pp.66-71 [18] S. Osher and J. A. Sethian.“Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations.”J-ournal of Computational Physics, 79:12-49, 1988. 2003, pp.755-768 [19] H.J. Zimmermann, Fuzzy Set Theory and Its Applications (Kluwer,Dordrecht,1991) [20] J.C. Bezdek,” Pattern Recognition with Fuzzy Objective Function Algorithms”.(Plenum, New York, 1981. [21] H.J. Zimmermann, “Fuzzy Set Theory and Its Applications”. (Kluwer,Dordrecht,1991) [22] Pan Lin, Chong-Xun Zheng and Yong Yang,” Model-based medical image segmentation: a level set approach” .Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on Volume 6, 15-19 June 2004 Page(s): 5541 - 5544 Vol.6 [23] Malladi, R., Sethian, J.A., Vemuri, B.C.” Shape modeling with front propagation: A level set approach”. IEEE Trans. on Pattern Analysis and Machine Intelligence 17 (1995) 158-175 [24] M.S. Yang, “A Survey of Fuzzy Clustering”, Mathl. Comput. Modelling Vol. 18(11) (1993) 1-16 well-separated clusters”. J.Cybernet.3 (1974)32~57 [25]Malladi, R., Sethian,J.A’’A unified approach to noise removal,image enhancement, and shape recovery”, 1996, Image Processing, IEEE Transactions on, Volume: 5 , Issue: 11 ,Nov. 1996, pp. 1554 - 1568 [26] Lefohn, A. E., Kniss, J., Hansen, C., and Whitaker, R.”Interactive deformation and visualization of level set surfaces using graphics hardware”. In IEEE Visualization (October 2003), pp. 497-504. [27] Z.U. Zchang and C.Y. Hsu, “Automatic Segmentation by Threshold Level Set Method with Adaptive Parameters”. CVGIP, 2005 [28] Aaron E. Lefohn, Joshua E. Cates, and Ross T. Whitaker ”Interactive, GPU-Based Level Sets for 3D Segmentation” Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112 [29] Hongchran Yu Dejun Wang Zesheng Tang“Level Set Method and Image Segmentation”,Medical Imaging and Augment Reality, Proceedings International workshop on,10-12 June 2001,pp.204-208 [30]Meihe Xu, Member, IEEE, Paul M. Thompson, Member, IEEE, and Arthur W. Toga, Member, IEEE “An Adaptive Level Set Segmentation on a Triangulated Mesh”Medical Imaging, IEEE Transactions on, Volume: 23 , Issue: 2 ,Feb. 2004, pp. 191 - 201 [31]Yen-Hsi Tsai,Stanley Osher” Level Set Methods in Image Science”, Image Processing, Proceedings. 2003 International Conference on, Volume: 2 , 14-17 ,Sept. 2003, pp.II- 631-4 vol.3 [32] D. Mumford and J. Shah, “Optimal approximation by piecewise smooth functions and associated variational problems,” Commun. Pure Appl. Math, vol. 42, pp. 577–685, 1989. [33]楊志弘,”幾何式主動輪廓之拓撲分析”,國立中興大學,應用數學研究所,93碩士論文 [34]邱孝賢,”超音波影像切割與三維圖像重構之研究”,成功大學,88年碩士論文 [35]盧先榮,”以奈維爾-史托克方程式與等為函數法求解自由液面流場之研究”,逢甲大學,土木及水利工程研究所,91碩士論文 [36]郭人豪,”以等為函數法求解含自由液面流場之研究”,逢甲大學,土木及水利工程研究所,90碩士論文
本文提出一種新型自適應閥值等位函數法(New Adaptive Threshold Level Set),以Fuzzy C-Means 演算法自動計算閥值,此方法是從Active Contours Without Edges改良而成的主動式輪廓線模型,應用在腦部MR影像的白質和灰質區域之自動化分割。由實驗結果顯示本文所提出之等位函數法能自動分割134張橫切面MR腦部影像白質和灰質區域部分,實驗分割結果以表面成像法(surface rendering)完成腦部灰質表面3D影像之重建。

In the paper proposes a New Adaptive Threshold Level Set Model. The model is based on the methodology of Active Contours Without Edges . The proposed method is then applied to the segmentation of gray matter in brain MR images. There are two threshold values, lower threshold and upper threshold, to be decided for locating the contours of the gray matter of the brain MR images. The threshold values are determined by Fuzzy C-Means Algorithm. By combining segmentation of 134 planar MR brain gray images. A three-dimensional model of the brain gray matter has been successfully constructed.
其他識別: U0005-2707200616594600
Appears in Collections:應用數學系所

Show full item record

Google ScholarTM


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