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研究生:林廷勳
研究生(外文):Ting-Xun Lin
論文名稱:結合互資訊與蟻群覓食演算法從事影像套合之研究
論文名稱(外文):Image Registration Using an Ant Colony Foraging Algorithm with Mutual Information
指導教授:張恆華
口試委員:張瑞益丁肇隆江明彰
口試日期:2015-07-17
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:工程科學及海洋工程學研究所
學門:工程學門
學類:綜合工程學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:中文
論文頁數:74
中文關鍵詞:影像套合蟻群最佳化演算法互資訊磁振影像空拍圖
外文關鍵詞:Image registrationant colony optimizationmutual informationmagnetic resonance imageaerial image
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影像套合對於工程上的研究與醫療診斷用途上是相當重要的,其目的為對多張影像處理,並將個別的資訊顯示在套合後的影像。現存影像套合的方法相當多樣化,本論文敘述應用蟻群最佳化演算法來從事影像套合之研究。基本的蟻群最佳化演算法包含有路徑的選擇規則及費洛蒙更新規則,本研究嘗試使用蟻群最佳化演算法的基礎概念,並改良此蟻群最佳化演算法求出每次螞蟻移動方向並且計算螞蟻尋找食物的移動距離,進而求得每次套合的形變量,並結合雙線性內插法得到影像套合的結果。使用蟻群最佳化演算法可以省略黏性流體影像套合方法中,求解非線性偏微分方程式的繁複計算。本研究所使用的蟻群最佳化演算法依賴蟻群之覓食習性,再結合資訊理論中熵的概念,進而求得互資訊,以提高影像套合的精確度,並縮短影像套合所需的時間。我們使用了大量不同的影像包含空拍圖及醫學影像等來評估此一新方法。實驗結果證實本研究所提出的方法可有效解決多種不同影像套合的問題,而套合結果也相當的準確。在與黏性流體方法比較後顯示,本研究所提出的方法不僅有較高的相關係數,而且花費較少的執行時間。本論文所提之方法在多種不同種類的影像套合應用中具有相當的潛力。

Image registration is very important for a wide variety of image processing applications in engineering and medicine. It provides lots of image information for further analysis in many fields. There are many image registration methods being proposed. This thesis describes a new image registration algorithm using an ant colony optimization (ACO) approach. There are two fundamental properties in the proposed ACO process: the probabilistic transition and the pheromone update. We used the ACO algorithm to solve the direction and distance of advancement and combined linear interpolation to transform images. Thanks to the efficient ACO, complex calculation such as solving the Navier–Stokes partial differential equation is not necessary. The entropy condition in information theory was introduced to obtain interactive information in order to improve registration accuracy and reduce processing time. A wide variety of images including aerial images and medical images were used to evaluate this new method. Experimental results indicated that the proposed method efficiently performed registration and provided high accuracy. Comparing to the viscous fluid model method, our algorithm produced higher correlation coefficient scores but also spent less computation time. We believe that our algorithm is of potential in many image registration applications.

致謝 i
中文摘要 ii
Abstract iii
目錄 iv
圖目錄 vii
表目錄 xi
符號表 xii
第 1 章 緒論 1
1.1 研究背景 1
1.2 研究動機與目的 2
1.3 論文架構 3
第 2 章 文獻探討 4
2.1 影像基本概念 4
2.1.1 影像資料格式 4
2.1.2 影像梯度運算子 6
2.2 影像套合理論 6
2.2.1 影像轉換 6
2.2.2 雙線性內插法(Bilinear interpolation) 8
2.3 套合效果評估標準 10
2.3.1 差方和 10
2.3.2 相關係數 10
2.4 常見影像套合相關方法 11
2.4.1 光流場(Optical flow)影像轉換 11
2.4.2 黏性流體(Viscous fluid)介質影像轉換 12
2.4.3 物體力(Body force)方程式 14
2.5 蟻群最佳化演算法(Ant colony optimization, ACO) 14
2.5.1 螞蟻系統(Ant System, AS)簡介 14
2.5.2 蟻群最佳化演算法之基本理論 17
2.5.3 蟻群最佳化演算法之演進 19
2.5.4 以蟻群最佳化演算法為基礎之影像套合 20
第 3 章 蟻群最佳化演算法用於套合之設計 23
3.1 方法流程 23
3.2 蟻群覓食演算法 25
3.2.1 決定食物的種類 25
3.2.2 選擇前進之大致方向 26
3.2.3 計算搜尋之距離 26
3.2.4 更新費洛蒙濃度 27
3.3 疊代過程 27
3.4 權重函數 28
3.4.1 互資訊(Mutual information) 28
3.4.2 函數設定 30
3.5 高斯平滑(Gaussian smoothing) 30
第 4 章 實驗與結果 32
4.1 實驗說明 32
4.2 參數統計 32
4.3 模擬影像 38
4.3.1 棋盤影像 38
4.3.2 C字型影像 39
4.3.3 橢圓形影像 42
4.4 一般影像 46
4.4.1 人物影像 46
4.4.2 空拍影像 51
4.5 醫學影像 55
4.5.1 膝蓋影像 55
4.5.2 腦部影像 58
4.5.3 去頭殼(Skull stripped)腦部影像 61
4.6 方法比較 64
第 5 章 結論與未來展望 70
5.1 結論 70
5.2 未來展望 71
參考文獻 72



[1]J. Modersitzki, Numerical methods for image registration: Oxford university press, 2003.
[2]A. K. Jain, Fundamentals of digital image processing: prentice-Hall Englewood Cliffs, 1989.
[3]M. D. Roger P. Woods. "AIR - Automated Image Registration," http://bishopw.loni.ucla.edu/air5/.
[4]D. L. Wang H, O''Daniel J, Mohan R, Garden AS, Ang KK, Kuban DA, Bonnen M, Chang JY, Cheung R., “Validation of an accelerated ''demons'' algorithm for deformable image registration in radiation therapy.,” Physics in Medicine and Biology, 2005.
[5]W. R. Crum, T. Hartkens, and D. Hill, “Non-rigid image registration: theory and practice,” The British Journal of Radiology, 2014.
[6]W. Peng, R. Tong, G. Qian, and J. Dong, "A constrained ant colony algorithm for image registration," Computational Intelligence and Bioinformatics, pp. 1-11: Springer, 2006.
[7]H. Rezaei, M. Shakeri, S. Azadi, and K. Jaferzade, “Multimodality image registration utilizing ant colony algorithm,” in Machine Vision, 2009. ICMV''09. Second International Conference on, 2009, pp. 49-53.
[8]H. Zhang, Y. Sun, B. Zhai, and Y. Wang, “Ant colony optimization image registration algorithm based on wavelet transform and mutual information,” in Fifth International Conference on Digital Image Processing, 2013, pp. 88781J-88781J-6.
[9]W. Wei, W. Lin, L. Liu, and Z. Q. Hu, “2D-3D Medical Image Registration Based on Ant Colony Algorithm,” in Applied Mechanics and Materials, 2014, pp. 267-273.
[10]R. C. Gonzalez, and R. E. Woods, Digital image processing. 2002: publishing house of electronics industry, 2002.
[11]洪維恩, “Matlab7 程式設計, 旗標出版股份有限公司, 台北,” 2006.
[12]J. Tian, W. Yu, and S. Xie, “An ant colony optimization algorithm for image edge detection,” in Evolutionary Computation, 2008. CEC 2008.(IEEE World Congress on Computational Intelligence). IEEE Congress on, 2008, pp. 751-756.
[13]R. M. Haralick, and L. G. Shapiro, “Image segmentation techniques,” in 1985 Technical Symposium East, 1985, pp. 2-9.
[14]Z. Yi, "Nonrigid Image Registration Using Physically Based Models," 2006.
[15]J. B. A. Maintz, and M. A. Viergever, “A survey of medical image registration,” vol. 2, pp. 36, 1998.
[16]B. Zitova, and J. Flusser, “Image registration methods: a survey,” ELSEVIER, 2003.
[17]劉鴻明, 蔡孟達, and 張元翔, “應用於影像縮放技術之內插法評估研究,” Chung Hua Journal of Science and Engineering, vol. 3, no. 5, pp. 43-49, 2005.
[18]J.-P. Thirion, “Image matching as a diffusion process: an analogy with Maxwell''s demons,” Medical image analysis, vol. 2, no. 3, pp. 243-260, 1998.
[19]B. Seibold, “A compact and fast Matlab code solving the incompressible Navier-Stokes equations on rectangular domains mit18086 navierstokes. m,” 2008.
[20]H.-H. Chang, and C.-Y. Tsai, “Adaptive registration of magnetic resonance images based on a viscous fluid model,” Computer Methods and Programs in Biomedicine, vol. 117, no. 2, pp. 80-91, 2014.
[21]G. E. Christensen, “Deformable shape models for anatomy,” Washington University Saint Louis, Mississippi, 1994.
[22]C. G. Morten Bro-nielsen “Fast Fluid Registration of Medical Images,” 1996.
[23]M. Dorigo, V. Maniezzo, and A. Colorni, “Ant system: optimization by a colony of cooperating agents,” Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, vol. 26, no. 1, pp. 29-41, 1996.
[24]M. Perretto, and H. S. Lopes, “Reconstruction of phylogenetic trees using the ant colony optimization paradigm,” Genetics and Molecular Research, vol. 4, no. 3, pp. 581-589, 2005.
[25]M. Dorigo, V. Maniezzo, A. Colorni, and V. Maniezzo, “Positive feedback as a search strategy,” 1991.
[26]M. Dorigo, and L. M. Gambardella, “Ant colony system: a cooperative learning approach to the traveling salesman problem,” Evolutionary Computation, IEEE Transactions on, vol. 1, no. 1, pp. 53-66, 1997.
[27]楊郁仙, “基於螞蟻演算法與路口延滯時間之最短時間路徑規劃,” 中興大學資訊科學與工程學系所學位論文, pp. 1-49, 2013.
[28]楊淑瑩, 模式識別與智能計算:Matlab的技術實現: 電子工業出版社, 2011.
[29]M. Dorigo, and L. M. Gambardella, “Ant colonies for the travelling salesman problem,” BioSystems, vol. 43, no. 2, pp. 73-81, 1997.
[30]P. Huang, H. Cao, and S. Luo, “An artificial ant colonies approach to medical image segmentation,” Computer Methods and Programs in Biomedicine, vol. 92, no. 3, pp. 267-273, 2008.
[31]J. Edmonds, “Matroids and the greedy algorithm,” Mathematical programming, vol. 1, no. 1, pp. 127-136, 1971.
[32]F. Maes, D. Vandermeulen, and P. Suetens, “Comparative evaluation of multiresolution optimization strategies for multimodality image registration by maximization of mutual information,” Medical image analysis, vol. 3, no. 4, pp. 373-386, 1999.
[33]R. M. Gray, Entropy and information theory: Springer Science & Business Media, 2011.
[34]C.-L. Yeh, “SPECT 基於互資訊和內插 CT 下應用影像特徵做 CT 與腦部影像對位,” 臺北科技大學電腦與通訊研究所學位論文, pp. 1-81, 2005.
[35]E. D''Agostino, F. Maes, D. Vandermeulen, and P. Suetens, “A viscous fluid model for multimodal non-rigid image registration using mutual information,” Medical image analysis, vol. 7, no. 4, pp. 565-575, 2003.


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