基于互信息的医学图像配准方法研究
|
摘要
医学图像配准技术已经被应用于心脏病诊断和包括脑瘤在内的各种各样的神经混乱诊断研究中。图像配准是使两幅图像上的对应点达到空间上一致的一个过程。这种一致是指人体上的同一特征点在两张匹配图像上有相同的空间位置。多模态生物医学图像配准在医疗诊断、治疗方案的制定,以及身体机能的研究等方面起到越来越大的作用。如何将这些多模态信息融合在一起是目前研究的重点,目前,该融合主要是基于图像亮度信息的配准方法。该类方法通过最大化图像间的相似度函数达到配准的目的。
基于最大互信息的配准技术目前是多模医学图像配准中应用比较普遍的一种方法。这种方法是用两幅图像的联合概率分布与完全独立时的概率分布的广义距离来估计互信息,并作为多模态医学图像配准的测度。当两幅基于共同的解剖结构的图像达到最佳配准时,它们的对应象素的灰度互信息应为最大。但配准过程中使用的目标函数往往会出现参数变化非凸且不光滑的现象。传统的局部最优方法通常不能得到较好的结果。因而,迫切的需要一种全局寻优的方法来解决这个问题。
本论文首先研究2D-2D的图像配准,分别用粒子群算法(PSO)、Powell方法、量子行为的粒子群算法(QPSO)和QPSO算法与Powell法结合的方法对2D-2D的医学图像进行配准并比较四种算法的结果。其次研究3D-3D的多模医学图像配准,分别用QPSO算法和QPSO算法与Powell法结合的方法对3D-3D的多模医学图像进行配准并把结果同“金标准”网站上的结果进行比较。通过比较发现在对2D-2D图像配准和3D-3D多模医学图像配准中QPSO算法的实际搜索效果、收敛速度和稳定性等均优于传统的Powell算法和PSO算法。
实验结果证明以互信息作为相似性测度,采用基于小波变换的多分辨率策略,将量子行为的粒子群优化算法(QPSO)与Powell法结合起来对二维和三维的图像进行配准能够有效地克服互信息函数的局部极值,大大地提高配准精度和速度,精度达到亚像素级。该问题的解决为医学图像配准在医疗诊断、治疗方案的制定以及身体机能的研究等方面提供了具有实际意义的方法。
Medical image registration has been applied to the diagnosis of cardiac studies and a variety neurological disorders including brain tumors. Image registration is the process of aligning two images so that corresponding features of the inages can be easily related. Registration using different modalities, or geometric alignment of two-dimensional and three-dimensional image data, is becoming increasingly important in diagnosis, treatment planning, functional studies, computer-guided therapies, and in biomedical research. Registration based on intensity values usually requires optimization of some similarity function between the images.
Maximization of mutual information(MI) of intensities is one of the most popular registration methods for multimodal medical image registration.This method measures the statistical dependence between the image intensities of corresponding voxels in two inages,this statiatical dependence is maximal when the images are totally aligned. Unfortunately, Local optimization techniques frequently fail because these functions with respect to transformation parameters are generally no convex and irregular and, therefore, global methods are often required.
In this thesis we study firstly the image registration of 2D to 2D, we used respectively Particle Swarm Optimization algorithm (PSO),Powell method, Quantum-Behaved Particle Swarm Optimization (QPSO) and a hybrid algorithm combined by QPSO algorithm and Powell’s method to solve the imgae registration of 2D to 2D and compare their results. Secondly, we study the multimodal medical image registration of 3D to 3D, we used QPSO and a hybrid algorithm combined by QPSO algorithm and Powell’s method to solve the multimodal medical image registration of 3D to 3D and compare their results with the website results of the gold standard. We find that the performances (namely the result of search, speed of convergence, stability, and so on) of QPSO are more efficiently in the image registration of 2D to 2D and the multimodal medical image registration of 3D to 3D than Powell and PSO.
This paper proposes a registration method based on wavelet representation. In this method the mutual information is used as the similarity measure and a hybrid algorithm combined by QPSO algorithm and Powell’s method as the search technique. This method is applied to the 2D and 3D image registration. Experiments results shows that this image registration method could efficiently restrain local maxima of mutual information function and improve accuracy and speed. And it can achieve the subvoxel accuracy.This solution provides a realistic method in medical image registration to use in the diagnosis, treatment planning, functional studies, and so on.
基于最大互信息的配准技术目前是多模医学图像配准中应用比较普遍的一种方法。这种方法是用两幅图像的联合概率分布与完全独立时的概率分布的广义距离来估计互信息,并作为多模态医学图像配准的测度。当两幅基于共同的解剖结构的图像达到最佳配准时,它们的对应象素的灰度互信息应为最大。但配准过程中使用的目标函数往往会出现参数变化非凸且不光滑的现象。传统的局部最优方法通常不能得到较好的结果。因而,迫切的需要一种全局寻优的方法来解决这个问题。
本论文首先研究2D-2D的图像配准,分别用粒子群算法(PSO)、Powell方法、量子行为的粒子群算法(QPSO)和QPSO算法与Powell法结合的方法对2D-2D的医学图像进行配准并比较四种算法的结果。其次研究3D-3D的多模医学图像配准,分别用QPSO算法和QPSO算法与Powell法结合的方法对3D-3D的多模医学图像进行配准并把结果同“金标准”网站上的结果进行比较。通过比较发现在对2D-2D图像配准和3D-3D多模医学图像配准中QPSO算法的实际搜索效果、收敛速度和稳定性等均优于传统的Powell算法和PSO算法。
实验结果证明以互信息作为相似性测度,采用基于小波变换的多分辨率策略,将量子行为的粒子群优化算法(QPSO)与Powell法结合起来对二维和三维的图像进行配准能够有效地克服互信息函数的局部极值,大大地提高配准精度和速度,精度达到亚像素级。该问题的解决为医学图像配准在医疗诊断、治疗方案的制定以及身体机能的研究等方面提供了具有实际意义的方法。
Medical image registration has been applied to the diagnosis of cardiac studies and a variety neurological disorders including brain tumors. Image registration is the process of aligning two images so that corresponding features of the inages can be easily related. Registration using different modalities, or geometric alignment of two-dimensional and three-dimensional image data, is becoming increasingly important in diagnosis, treatment planning, functional studies, computer-guided therapies, and in biomedical research. Registration based on intensity values usually requires optimization of some similarity function between the images.
Maximization of mutual information(MI) of intensities is one of the most popular registration methods for multimodal medical image registration.This method measures the statistical dependence between the image intensities of corresponding voxels in two inages,this statiatical dependence is maximal when the images are totally aligned. Unfortunately, Local optimization techniques frequently fail because these functions with respect to transformation parameters are generally no convex and irregular and, therefore, global methods are often required.
In this thesis we study firstly the image registration of 2D to 2D, we used respectively Particle Swarm Optimization algorithm (PSO),Powell method, Quantum-Behaved Particle Swarm Optimization (QPSO) and a hybrid algorithm combined by QPSO algorithm and Powell’s method to solve the imgae registration of 2D to 2D and compare their results. Secondly, we study the multimodal medical image registration of 3D to 3D, we used QPSO and a hybrid algorithm combined by QPSO algorithm and Powell’s method to solve the multimodal medical image registration of 3D to 3D and compare their results with the website results of the gold standard. We find that the performances (namely the result of search, speed of convergence, stability, and so on) of QPSO are more efficiently in the image registration of 2D to 2D and the multimodal medical image registration of 3D to 3D than Powell and PSO.
This paper proposes a registration method based on wavelet representation. In this method the mutual information is used as the similarity measure and a hybrid algorithm combined by QPSO algorithm and Powell’s method as the search technique. This method is applied to the 2D and 3D image registration. Experiments results shows that this image registration method could efficiently restrain local maxima of mutual information function and improve accuracy and speed. And it can achieve the subvoxel accuracy.This solution provides a realistic method in medical image registration to use in the diagnosis, treatment planning, functional studies, and so on.
引文
1. D.L.G.Hill,C.Studholme,and D.J.Hawkes, Voxel similarity measures for automated image registration, SPIE,vol.2359, 1994. 205-216
2. A.Collignon, F.Maes, D.Delaere, D.Vandermeulen, P.Suetens,and G.Marchal,Automated multi- modality image registration using information theory, Information Processing in Medical Imaging(IPMI`95()Y.Bizais,C.Barilot, and R.Dipaola,Eds.)Dordrecht:Kluwer, 1995. 263-274
3. W.M.Wells, P.Viola, H.Atsumi, S.Nakajima,and R.Kikinis, Multi-modal volume registration by maximization of mutual information, Medical Image Anal.1, 1996.35-51
4. F.Maes, A.Collignon, D.Vandermeulen, G.Marchal,and P.Suetens, Multi-modality image registration by maximization of mutual information, IEEE Med. Imaging, vol.16, 1997. 187-198
5. P.A Viola, Alignment by maximization of mutual information, PhD thesis,Massachusetts Instiyute of Technology,1995
6. I.Vajda, Theory of statistic inference and information, Dordrecht, The Netherlands: Kluwer, 1989
7. C.Studholme, D.L.G.Hill, and D.J.Hawkes, An overlap invariant entropy measure of 3D medical image alignment, Pattern Recognition, 32(1), 1999. 71-86
8. W.H. Press, B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling, Numerical recipes in C: the art of scientific computing, 2nd ed.,Cambridge, U.K.;Cambridge University Press, 1999. 394-455
9.席少霖,赵凤治.最优化计算方法[M ].上海科学技术出版社, 1983. 183 - 190
10.薛家庆.最优化原理和方法[M ].东北工学院出版社, 1981. 155 - 172
11.陈白丽,郝娟.一个进一步改进的Powell直接优化法[ J ].计算机工程与设计, 1999. 8, 16-19
12. J.P.W.Pluim, J.B.A.Maintz, M.A.Viergever. Mutual information based registration of medical images :a survey[J ] . IEEE Transactions On Medical Imaging , 2003 . 22 (8):986-1004
13. F.Maes, D.Vandermeulen, P. Suetens, Comparative evaluation of multiresolution optimizati- on strategies for multimodality image registration by maximization of mutual information [ J ] .Medical Image Analysis. 1999. 3 (4) : 373 - 386.
14. Eberhart R C, Kennedy J., A new optimizer using particles swarm theory[C] .Proc Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, 1995.39-43
15. Kennedy J, Eberhart R C, Particle swarm optimization[C] Proceedings of IEEE International Conference on Neural Networks.IEEE Service Center, Piscataway, NJ, 1995. 1942- 1948
16. SUN Jun, XU Wenbo. A global search strategy of quantum-behaved particle swarm optimization: proceedings of IEEE Conference on Cybernetics and Intelligent Systems[ C ]. [ S. l. ] : [ s. n. ] , 2004.111-116
17. SUN Jun, FENG Bin, XU Wenbo. Particle swarm optimization with particles having quantum behavior: p roceedings of 2004 Congress on Evolutionary Computation[ C ]. [ S. l. ] : [ s. n. ] , 2004: 325-331
18. C RMaurer. Registration of 2D images using weighted geometrical feature[ J ]. IEEE TransMedical Image, 1996, 15: 836 - 849
19. Van den Bergh F, Engelbrecht A.P. A New Locally Convergent Particle Swarm Optimiser. IEEE Conference on Systems, Man and Cybernetics, 2002,vol 3, 94-99
20. Van den Bergh, F. An Analysis of Particle Swarm Optimization. Pretorai: PhD Dissertation of University of Pretoria, 2001
21.Clerc M., Kennedy,J. The Particle Swarm Optimization-Explosion, Stability, and Convergence in a Multidimensional Complex Space. IEEE Transactions on Evolutionary Computation, 2002, 6(1): 58-73
22. Clerc M. The Swarm and the Queen: Towards a Deterministic and Adaptive Particle Swarm Optimization. Proceedings of IEEE Congress on Evolutionary Computation, Washington, DC, 1999, 1951-1957
23. Shi Y, Eberhart R.C. A Modified Particle Swarm Optimizer. IEEE International Conference on Evolutionary Computation (CEC 98), Piscataway, NJ, 1998, 69-73
24. Sun J, C.H.Lai, Wenbo Xu. Quantum-behaved Particle Swarm Optimization and Its Application. Journal of Computer and Mathematics with Applications, U.K
25.黄顺基.自然辩证法概论.国家教委社科司组编:高等教育出版社,1996
26.曾谨言.量子力学(卷II).3版.北京:科学出版社, 2000
27. Bin Feng, Wenbo Xu. Adaptive Particle Swarm Optimization Based on Quantum Oscillator Model. IEEE Conference on Cybernetics & Intelligent Systems, Singapore, 2004, 291-294
28. Yutaro Yamamura; Hyoungseop Kim; Akiyoshi Yamamoto. A Method for Image Registration by Maximization of Mutual Information. SICE-ICASE,2006.International Joint Conference Oct. 2006.1469-1472
29. Seok Lee, Minseok Choi, Hyungmin Kim, Park, F.C. Geometric Direct Search Algorithmsfor Image Registration[J]. Image Processing,IEEE Transactions Volume 16(9) Sept. 2007.2215– 2224
30. Ximiao Cao, Qiuqi Ruan. A Survey on Evaluation Methods for Medical Image Registration. Complex Medical Engineering,2007.CME2007.IEEE/ICME International Conference May 2007.718– 721
31. Wang Anna,Wu Jie,Zhang Xinhua,Tao Ran. A Novel Medical Image Registration Algorithm Based on PSO and Wavelet Transformation Combined with 2v-SVM.Innovative Computing, Information and Control,2007.ICICIC07.Second International Conference Sept. 2007.584-584
32.罗述谦,周果宏.医学图象处理与分析[M].科学出版社,2003.140-202
33.杨帆,张汗灵.蚁群算法和Powell法结合的多分辨率三维图像配准[J].电子与信息学报,2007, 29(3):622-625
34.万蕊,梅炯,李明禄.基于最大互信息和相关法的医学图象配准[J].中国图象图形学报,2003,8(A),spec:834-838
35. J. Tsao, Interpolation artifacts in multimodality image registration based on maximization ofmutual information. IEEETrans.Med. Imag., vol. 22,no. 7, Jul. 2003. 854–864
36. M. P. Wachowiak, R. Smol′?kov′a, Y. Zheng, J. M. Zurada, and A. S. Elmaghraby. An approach to multimodal biomedical image registration utilizingparticle swarm optimization. IEEE Trans. Evol. Comput., vol. 8,no. 3, Jun. 2004. 289–301
2. A.Collignon, F.Maes, D.Delaere, D.Vandermeulen, P.Suetens,and G.Marchal,Automated multi- modality image registration using information theory, Information Processing in Medical Imaging(IPMI`95()Y.Bizais,C.Barilot, and R.Dipaola,Eds.)Dordrecht:Kluwer, 1995. 263-274
3. W.M.Wells, P.Viola, H.Atsumi, S.Nakajima,and R.Kikinis, Multi-modal volume registration by maximization of mutual information, Medical Image Anal.1, 1996.35-51
4. F.Maes, A.Collignon, D.Vandermeulen, G.Marchal,and P.Suetens, Multi-modality image registration by maximization of mutual information, IEEE Med. Imaging, vol.16, 1997. 187-198
5. P.A Viola, Alignment by maximization of mutual information, PhD thesis,Massachusetts Instiyute of Technology,1995
6. I.Vajda, Theory of statistic inference and information, Dordrecht, The Netherlands: Kluwer, 1989
7. C.Studholme, D.L.G.Hill, and D.J.Hawkes, An overlap invariant entropy measure of 3D medical image alignment, Pattern Recognition, 32(1), 1999. 71-86
8. W.H. Press, B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling, Numerical recipes in C: the art of scientific computing, 2nd ed.,Cambridge, U.K.;Cambridge University Press, 1999. 394-455
9.席少霖,赵凤治.最优化计算方法[M ].上海科学技术出版社, 1983. 183 - 190
10.薛家庆.最优化原理和方法[M ].东北工学院出版社, 1981. 155 - 172
11.陈白丽,郝娟.一个进一步改进的Powell直接优化法[ J ].计算机工程与设计, 1999. 8, 16-19
12. J.P.W.Pluim, J.B.A.Maintz, M.A.Viergever. Mutual information based registration of medical images :a survey[J ] . IEEE Transactions On Medical Imaging , 2003 . 22 (8):986-1004
13. F.Maes, D.Vandermeulen, P. Suetens, Comparative evaluation of multiresolution optimizati- on strategies for multimodality image registration by maximization of mutual information [ J ] .Medical Image Analysis. 1999. 3 (4) : 373 - 386.
14. Eberhart R C, Kennedy J., A new optimizer using particles swarm theory[C] .Proc Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, 1995.39-43
15. Kennedy J, Eberhart R C, Particle swarm optimization[C] Proceedings of IEEE International Conference on Neural Networks.IEEE Service Center, Piscataway, NJ, 1995. 1942- 1948
16. SUN Jun, XU Wenbo. A global search strategy of quantum-behaved particle swarm optimization: proceedings of IEEE Conference on Cybernetics and Intelligent Systems[ C ]. [ S. l. ] : [ s. n. ] , 2004.111-116
17. SUN Jun, FENG Bin, XU Wenbo. Particle swarm optimization with particles having quantum behavior: p roceedings of 2004 Congress on Evolutionary Computation[ C ]. [ S. l. ] : [ s. n. ] , 2004: 325-331
18. C RMaurer. Registration of 2D images using weighted geometrical feature[ J ]. IEEE TransMedical Image, 1996, 15: 836 - 849
19. Van den Bergh F, Engelbrecht A.P. A New Locally Convergent Particle Swarm Optimiser. IEEE Conference on Systems, Man and Cybernetics, 2002,vol 3, 94-99
20. Van den Bergh, F. An Analysis of Particle Swarm Optimization. Pretorai: PhD Dissertation of University of Pretoria, 2001
21.Clerc M., Kennedy,J. The Particle Swarm Optimization-Explosion, Stability, and Convergence in a Multidimensional Complex Space. IEEE Transactions on Evolutionary Computation, 2002, 6(1): 58-73
22. Clerc M. The Swarm and the Queen: Towards a Deterministic and Adaptive Particle Swarm Optimization. Proceedings of IEEE Congress on Evolutionary Computation, Washington, DC, 1999, 1951-1957
23. Shi Y, Eberhart R.C. A Modified Particle Swarm Optimizer. IEEE International Conference on Evolutionary Computation (CEC 98), Piscataway, NJ, 1998, 69-73
24. Sun J, C.H.Lai, Wenbo Xu. Quantum-behaved Particle Swarm Optimization and Its Application. Journal of Computer and Mathematics with Applications, U.K
25.黄顺基.自然辩证法概论.国家教委社科司组编:高等教育出版社,1996
26.曾谨言.量子力学(卷II).3版.北京:科学出版社, 2000
27. Bin Feng, Wenbo Xu. Adaptive Particle Swarm Optimization Based on Quantum Oscillator Model. IEEE Conference on Cybernetics & Intelligent Systems, Singapore, 2004, 291-294
28. Yutaro Yamamura; Hyoungseop Kim; Akiyoshi Yamamoto. A Method for Image Registration by Maximization of Mutual Information. SICE-ICASE,2006.International Joint Conference Oct. 2006.1469-1472
29. Seok Lee, Minseok Choi, Hyungmin Kim, Park, F.C. Geometric Direct Search Algorithmsfor Image Registration[J]. Image Processing,IEEE Transactions Volume 16(9) Sept. 2007.2215– 2224
30. Ximiao Cao, Qiuqi Ruan. A Survey on Evaluation Methods for Medical Image Registration. Complex Medical Engineering,2007.CME2007.IEEE/ICME International Conference May 2007.718– 721
31. Wang Anna,Wu Jie,Zhang Xinhua,Tao Ran. A Novel Medical Image Registration Algorithm Based on PSO and Wavelet Transformation Combined with 2v-SVM.Innovative Computing, Information and Control,2007.ICICIC07.Second International Conference Sept. 2007.584-584
32.罗述谦,周果宏.医学图象处理与分析[M].科学出版社,2003.140-202
33.杨帆,张汗灵.蚁群算法和Powell法结合的多分辨率三维图像配准[J].电子与信息学报,2007, 29(3):622-625
34.万蕊,梅炯,李明禄.基于最大互信息和相关法的医学图象配准[J].中国图象图形学报,2003,8(A),spec:834-838
35. J. Tsao, Interpolation artifacts in multimodality image registration based on maximization ofmutual information. IEEETrans.Med. Imag., vol. 22,no. 7, Jul. 2003. 854–864
36. M. P. Wachowiak, R. Smol′?kov′a, Y. Zheng, J. M. Zurada, and A. S. Elmaghraby. An approach to multimodal biomedical image registration utilizingparticle swarm optimization. IEEE Trans. Evol. Comput., vol. 8,no. 3, Jun. 2004. 289–301