臺灣博碩士論文加值系統

在醫學影像的領域中，經常會比對來自不同物體模型的資料，以比較分析兩者之間的差異，因此需要一個校準方法，找出兩組物體的關連性，並且將它們對齊在一起。Besl與McKay在1992年提出了ICP（iterative closest point）演算法，可以應用在不同幾何結構的物體上，藉由尋找兩組物體間距離最近的對應關係，計算出一組最小平方的幾何轉換矩陣，反覆縮小兩組物體間的mean square distance，最後得到最佳的幾何轉換矩陣，將兩組物體對齊在一起。在本文中介紹ICP演算法做為校準方法，以人體腦部MRI腦部影像為實驗對象，從兩組不同的MRI影像中擷取出腦部白質的幾何結構，將兩組不同的腦部白質結構校準，顯示校準的結果，並且比較在不同情形下校準的差異。

In medical image processing, there exists a need to align two object models in order to compare the difference. For the two object models, we want to find the relation between them and to know how to superimpose one object with the other. Besl and McKay proposed the ICP（iterative closest point）algorithm in 1992, which was a general-purpose, representation-independent method to align different object models. ICP algorithm iteratively minimizes the distance between two object models and computes a least square transformation matrix. We introduce the ICP algorithm and use it to align the 3-D geometric models of brains. Experiment shows the result of alignment of different brain models.

中文摘要 i
英文摘要 ii
致謝 iii
目錄 iv
圖表目錄 v
第一章 簡介 1
前言 1
論文架構 2
第二章 相關研究 3
第三章 ICP演算法 7
基本理論 7
衍生應用 15
第四章 實驗步驟 19
校準：尋找對應關係 20
區域最佳值 21
資料來源與前置處理 22
顯示校準結果 25
第五章 實驗結果與討論 26
整體校準實驗 26
部分校準實驗 39
不同形式校準實驗 43
第六章 結論 47
參考文獻 49

[1]H. Alt and L. J. Guibas, “Discrete Geometric Shapes: Matching, Interpolation, and Approximation：A Survey”, Handbook of Computational Geometry, eds. J. -R. Sack and J. Urrutia （Elsevier Science Publishers B.V. North-Holland, Amsterdam, 1999）, pp. 121--153.
[2]J. B. Antoine Maintz and Max A. Viergever, “An Overview of Medical Image Registration Methods”, in Symposium of the Belgian hospital physicists association（SBPH-BVZF）, vol. 12, pp. V:1-22, 1996/1997
[3]P. J. Besl and N. D. Mckay, “A Method for Registration of 3-D Shapes”, IEEE Trans. Pattern Anal. Mach. Intell., vol 14, pp.239-256, 1992.
[4]Brian Curless and Marc Levoy, “A Volumetric Method for Building Complex Models from Range Images”, Proc. Computer Graphics（SIGGRAPH ’96）, August 1996.
[5]J. Feldmar, J. Declerck, G. Malandian, and N. Ayache, “Extension of the ICP Algorithm to Nonrigid Intensity-Based Registration of 3D Volumes”, Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, pp. 84-93, June 1996.
[6]A. Kapoutsis, C. P. Vavoulidis, and I. Pitas, “Morphological Iterative Closest Point Algorithm”, IEEE Trans. Image Processing, vol.8, NO 11, November 1999.
[7]W. E. Lorensen and Harvey E. Cline, “Marching Cubes：A High Resolution 3D Surface Construction Algorithm”, Proc. Computer Graphics（SIGGRAPH ’87）, 21(4): 163—169, July 1987.
[8]C. R. Maurer, Georges B. Aboutanos, Benoit M. Dawant, Robert J. Maciunas, and J. M. Fitzpatrick, “Registration of 3-D Images Using Weighted Geometrical Features”, IEEE Trans. Medical Imaging, vol. 15, no. 6, December 1996.
[9]C. R. Maurer and J. M. Fitzpatrick, “A review of medical image registration”, In Interactive Image-Guided Neurosurgery, chapter 3, pages 17--44. American Association of Neurological Surgeons, 1993, pp. 17-44.
[10]C. R. Maurer, R. J. Maciunas and J. M. Fitzpatrick, “Registration of head CT images to physical space using a weighted combination of points and surfaces”, IEEE Trans. Medical Imaging, vol. 17, no. 5, October 1998.
[11]G. Turk and M. Levoy, “Zippered Polygon Meshes from Range Images”, Proc. Computer Graphics（SIGGRAPH ’94）, pp. 311-318, 1994.