医学图像配准中的相似性测试研究
|
摘要
医学成像技术的蓬勃发展为临床提供了大量实用的解剖及功能方面的影像数据。临床上常常需要配准并融合同模态或者不同模态的医疗图像,以获取更全面的病患信息,指导临床诊断和治疗。医学图像配准技术就成了目前医学图像处理和分析中基础及关键技术,具有重要的临床应用价值,而配准的相似性测度直接决定了配准的鲁棒性和有效性。
本文对刚体图像、序列刚体图像以及非刚体图像现有的配准方法中的相似性测度进行了研究,针对其存在的不足,提出了适用于各种配准应用的新方法。本文的主要内容包括:
针对刚体医学图像配准的相似性测度进行了研究。为提高其配准鲁棒性,本文提出了一种新的自适应指数加权的互信息(Adaptive Exponential WeightedMutual Information,AEWMI)测度,该测度通过对互信息测度进行指数加权可以提高测度曲线的峰值尖锐性和平滑性;通过评估待配准图像的质量和分辨率大小来自适应确定指数权值,进一步提高了配准测度的鲁棒性和普适性。仿真实验结果表明,基于本文所提测度的配准方案,对图像噪声、分辨率差异等有较高的鲁棒性,且可有效地提高配准的成功率。
针对序列医学图像配准方法中的相似性测度进行了创新。传统的序列图像配准方法大多基于两幅图像的配准方法,并且在低信噪比的情况下面临着鲁棒性问题。为解决此问题,本文提出了一种全新的适用于序列图像配准的相似性测度--归一化高阶互累积量系数(Normalized Higher-order Cross-cumulant Coefficient,NHCC)。分析表明:本文提出的NHCC测度不仅可以同时敏感多幅图像之间的相关程度,而且能免疫加性高斯噪声对配准的影响。仿真和实验结果验证了该测度的有效性和优越性。
针对非刚性空间变换的数字减影图像(Digital Subtraction Angiography,DSA)配准,本文提出了一种新的基于块图像内容自适应选择相似性测度的匹配方法。它根据DSA图像的血流特性对块图像进行分类,对不同内容的块图像采用不同的相似性测度,分析表明该方法可有效地减小血管成分和图像模糊等对配准精度造成的影响,提高图像整体配准的鲁棒性。结合边缘控制点提取以及薄板样条(Thin Plate Spline,TPS)非线性变换,我们对医院采集到的DSA图像进行配准实验,实验结果验证了本文提出匹配方法的有效性。
Along with the fast development of the medical imaging technology,more and more anatomic and/or functional medical images could be obtained in clinic.It is always required to register mono-modality and multimodality medical images in order to obtain patients' all-round information,which is helpful in clinical diagnosis and therapy.Medical image registration is a key technology of medical image processing and analysis and has significant meanings in clinical analysis.
This dissertation mainly focuses on the registration methods for rigid images, rigid image sequences and non-rigid images.In view of their disadvantages,this dissertation proposes several new and robust registration methods respectively.The main contents of this dissertation are as follows:
In order to improve the robustness for rigid images registration,a new similarity measure for medical image registration,called Adaptive Exponential Weighted Mutual Information(AEWMI),is proposed.AEWMI could improve the smoothness and peak keenness performance of similarity measurement curve via adaptively weighting MI according to image quality and resolution.The experimental results,demonstrating the analyzing result,show that our method is robust to image noise or resolution difference and improves the registration robustness effectively.
This dissertation innovate the registration method for medical image sequences. A novel registration measure,called as Normalized Higher-order Cross-cumulant Coefficient(NHCC) is proposed.The analytical results show that the proposed NHCC cannot only easily capture the correlation information among the multiple variables simultaneously,but also suppress the additive Gaussian noise influence on the image registration results.While simulation results verify the effectiveness and robustness of the proposed measure,better experimental results of the digital subtraction angiography image registration are also obtained.
Under the non-rigid image registration frame,the dissertation focuses on studying an accurate and effective processing method for digital subtraction angiography(DSA) image registration.An image registration method was proposed to be based on the similarity measurement of block image content with self-adaptive selection.In the process of digital subtraction angiography image registration,block image content was classified with blood flow character and for different block image content different similarity measurement was selected with self-adaptation.The analyses and experimental results demonstrated that our method could decrease the influences of the image blurring and vessels on the accuracy of the DSA image registration.The proposed method can effectively improve the robustness and accuracy of DSA image registration.
本文对刚体图像、序列刚体图像以及非刚体图像现有的配准方法中的相似性测度进行了研究,针对其存在的不足,提出了适用于各种配准应用的新方法。本文的主要内容包括:
针对刚体医学图像配准的相似性测度进行了研究。为提高其配准鲁棒性,本文提出了一种新的自适应指数加权的互信息(Adaptive Exponential WeightedMutual Information,AEWMI)测度,该测度通过对互信息测度进行指数加权可以提高测度曲线的峰值尖锐性和平滑性;通过评估待配准图像的质量和分辨率大小来自适应确定指数权值,进一步提高了配准测度的鲁棒性和普适性。仿真实验结果表明,基于本文所提测度的配准方案,对图像噪声、分辨率差异等有较高的鲁棒性,且可有效地提高配准的成功率。
针对序列医学图像配准方法中的相似性测度进行了创新。传统的序列图像配准方法大多基于两幅图像的配准方法,并且在低信噪比的情况下面临着鲁棒性问题。为解决此问题,本文提出了一种全新的适用于序列图像配准的相似性测度--归一化高阶互累积量系数(Normalized Higher-order Cross-cumulant Coefficient,NHCC)。分析表明:本文提出的NHCC测度不仅可以同时敏感多幅图像之间的相关程度,而且能免疫加性高斯噪声对配准的影响。仿真和实验结果验证了该测度的有效性和优越性。
针对非刚性空间变换的数字减影图像(Digital Subtraction Angiography,DSA)配准,本文提出了一种新的基于块图像内容自适应选择相似性测度的匹配方法。它根据DSA图像的血流特性对块图像进行分类,对不同内容的块图像采用不同的相似性测度,分析表明该方法可有效地减小血管成分和图像模糊等对配准精度造成的影响,提高图像整体配准的鲁棒性。结合边缘控制点提取以及薄板样条(Thin Plate Spline,TPS)非线性变换,我们对医院采集到的DSA图像进行配准实验,实验结果验证了本文提出匹配方法的有效性。
Along with the fast development of the medical imaging technology,more and more anatomic and/or functional medical images could be obtained in clinic.It is always required to register mono-modality and multimodality medical images in order to obtain patients' all-round information,which is helpful in clinical diagnosis and therapy.Medical image registration is a key technology of medical image processing and analysis and has significant meanings in clinical analysis.
This dissertation mainly focuses on the registration methods for rigid images, rigid image sequences and non-rigid images.In view of their disadvantages,this dissertation proposes several new and robust registration methods respectively.The main contents of this dissertation are as follows:
In order to improve the robustness for rigid images registration,a new similarity measure for medical image registration,called Adaptive Exponential Weighted Mutual Information(AEWMI),is proposed.AEWMI could improve the smoothness and peak keenness performance of similarity measurement curve via adaptively weighting MI according to image quality and resolution.The experimental results,demonstrating the analyzing result,show that our method is robust to image noise or resolution difference and improves the registration robustness effectively.
This dissertation innovate the registration method for medical image sequences. A novel registration measure,called as Normalized Higher-order Cross-cumulant Coefficient(NHCC) is proposed.The analytical results show that the proposed NHCC cannot only easily capture the correlation information among the multiple variables simultaneously,but also suppress the additive Gaussian noise influence on the image registration results.While simulation results verify the effectiveness and robustness of the proposed measure,better experimental results of the digital subtraction angiography image registration are also obtained.
Under the non-rigid image registration frame,the dissertation focuses on studying an accurate and effective processing method for digital subtraction angiography(DSA) image registration.An image registration method was proposed to be based on the similarity measurement of block image content with self-adaptive selection.In the process of digital subtraction angiography image registration,block image content was classified with blood flow character and for different block image content different similarity measurement was selected with self-adaptation.The analyses and experimental results demonstrated that our method could decrease the influences of the image blurring and vessels on the accuracy of the DSA image registration.The proposed method can effectively improve the robustness and accuracy of DSA image registration.
引文
[1]Zitova B and Flusser J.Image registration methods:a survey[J].Image and Vision Computing.2003,21 (11):977-1000.
[2]Maintz JBA and Viergever MA.A survey of medical image registration[J].Medical Image Analysis.1998,2(1):l-36.
[3]Pluim JPW,Maintz JBA,Viergever MA.Mutual-information-based registration of medical images:A survey [J].IEEE Transactions on Medical Imaging,2003,22(8):986-1004.
[4]Maes F,Collignon A,Vandermeulen D,et al.Multimodality image registration by maximization of mutual information [J].IEEE Transactions on Medical Imaging,1997,16(2):187-198.
[5]Maes F,Vandermeulen D,Suetens P.Medical image registration using mutual information [J].Proceeding of the IEEE,2003,91(10):1699-1722.
[6]Jin J,Wang Q,Shen Y.Registering multiple medical images using the shared chain mutual information[J].Chinese Optics Letters.2007,5(7):389-392.
[7]Meijering EHW,Niessen WJ,Viergever MA.Retrospective motion correction in digital subtraction angiography:a review [J].IEEE Transactions on Medical Imaging,1999,18(1):2-21.
[8]Bentoutou Y,Taleb N.Automatic extraction of control points for digital subtraction angiography image enhancement [J].IEEE Transactions on Nuclear Science,2005,52(1):238-246.
[9]Bookstein FL.Principal warps:thin-plate splines and the decomposition of deformations [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1989,11(6):567-585.
[10]Wan R,Li ML.An Overview of Medical Image Registration[A].In:Proceedings of the Fifth International Conference on Computational Intelligence and Multimedia Applications[C],2003.
[11]http://www.bic.mni.mcgill.ca/brainweb
[12]朱翠玲.现代医学影响学[M].山东科学技术出版社,2000
[13]Brown LG.A survey of image registration techniques[J].ACM Computing Surveys,1992,24(4):326-376.
[14]Viola P and Wells WM.Alignment by maximization of mutual information[J].In:Proceedings of the 5th International Conference on Computer Vision,1995:16-23
[15]Studholme C,Hill DLG,and Hawkes DJ.An overlap invariant entropy measure of 3D medical image alignment[J].Pattern Recognition,1999,32(1):71-86.
[16]Pluim JPW,Maintz JBA,Viergever MA.Image registration by maximization of combined mutual information and gradient information[J].IEEE Transactions on Medical Imaging,2000,19(8):809-814.
[17]Maes F,Collignon A,Vandermeulen D,Marchal G,Suetens P.Multimodality image registration by maximization of mutual information[J].IEEE Transactions on Medical Imaging,1997,16:187-198.
[18]Sawhney HS,Kumar R.True multi-image alignment and its applications to mosaicing and lens distortion correction[J].IEEE Transactions on Pattern Analysis and Machine Intellingece,2001,21:235-243.
[19]Radau PE,Slomka PJ,Julin P,Svensson L and Wahlund LO.Evaluation of linear registration algorithms for brain SPECT and the errors due to hypoperfusion lesions[J].Medical Physics,2001,28(8):1660-1668.
[20]Studholme C,Novotny E,Zubal IG,Duncan JS.Estimating tissue deformation between functional images induced by intracranial electrode implantation using anatomical MRI[J].NeuroImage,2001,13(4):561-576.
[21]陈国良,王煦法,庄镇泉,等。遗传算法及其应用[M].北京:人民邮电出版社,1996.
[22]Nikou C,Heitz F,and Armspach JP.Robust voxel similarity metrics for the registration of dissimilar single and multimodal images[J].Pattern Recognition,1999,32(8):1351-1368.
[23]冈萨雷斯.数字图像处理[M],第二版.北京:电子工业出版社,2007.176-183
[24]Djamdji JP,Bajaoui A,Maniere R.Geometrical registration of images:the multiresolution approach[J].Photogrammetric Engineering and Remote Sensing,1993,53:645-653.
[25]Allen RL,Kamangar FA,and Stokely EM.Laplacian and Orthogonal Wavelet Pyramid Decompositions in Coarse-to-Fine Registration[J].IEEE Transactions on Signal Processing,1993,41(12):3536-3541.
[26]Lemieux L,Jagoe R.Effect of fiducial Marker localization on stereotactic target coordinate calculation in CT slices and radiographs[J].J.Of Physics in Medicine and Biology,1994,39:1915-1928.
[27]Savi A,Gilardi MC,Rizzo G,Pepi M,Landoni C,Ross C.Spatial registration of echocardiographic and positron emission tomographic heart studies[J].European Journal of Nuclear Medicine.1995,22(3):243-247.
[28]Chen G.TY,Pelizzari CA.Image correlation techniques in radiation therapy planing[J].Computerized Medical Imaging and Graphics.1989,13:235-240.
[29]梅跃松,杨树兴,莫波.基于Canny算子的改进的图像边缘检测方法[J].激光与红外,2006,36(3):501-503.
[30]严国萍,何俊峰.高斯.拉普拉斯边缘检测算子的扩展研究[J].华中科技大学学报,2006,34(10):21-23.
[31]Alpert NM,Bradshaw JF,Kennedy D,Correia JA.The principle axis transformation-a method for image registration[J].J.of Nuclear medicine.1990,31:1717-1722.
[32]Colligono A,Vandermeulen D,Suetens P,Marchal G 3D multimodality medical image registration using feature space clustering[J].Computer Vision,Virtual Reality and Robotics in Medicine,Lecture Notes in Computer Science,1995,905:195-204.
[33]Studholme C,Hill DLG,Hawkes DJ.Multiresolution voxel similarity measures for MR-PET registration[A].In:Proceedings of Informaion Processing in Medical Imaging[C],1995:287-298.
[34]Viola P and Wells WM.Alignment by maximization of mutual information[A].In:Proceedings of the 5th International Conference on Computer Vision[C], 1995:16-23.
[35]Pennee X,Cachier P,Ayache N.Tracking brain deformations in time sequences of 3D US images[J].Pattern Recognition Letters,2003,24:801-823.
[36]Boes JL,Meyer CR.Multi-variate mutual information for registration[J].Medical Image Computing and Computer-Assisted Intervention,Lecture Notes in Computer Science,1999,1678:606-612.
[37]Jin J,Wang Q,Shen Y.Nonlinear correlation measurement based registration for sequential images[A].In:Proceedings of IEEE Instrumentation and Measurement Technology Conference[C],Warsaw,Poland:2007:1-5.
[38]Studholme C,Hill DLG,Hawkes DJ.Incorporating connected region labeling into automated image registration using mutual information[J].Mathematical Methods in Biomedical Image Analysis.1996:23-31.
[39]Wang B,Shen Y.A method on calculating high-dimensional mutual information and its application to registration of multiple ultrasound images[J].Ultrasonics (2006),doi:10.1016/j.ultras.2006.07.012
[40]王海南,郝重阳,雷方元,张先勇.非刚性医学图像配准研究综述[J].计算机工程与应用.2005.11.180-184.
[41]Kern JP and Pattichis MS.Robust multispectral image registration using mutual-information models[J].IEEE Transactions on Geoscience and Remote Sensing,2007,45(5):1494-1504.
[42]Rosenfeld A and Kak AC.Digital Pictrure Processing Vol Ⅰ and Ⅱ[M].Academic Press,Orlando,FL,1982.
[43]Maes F,Vandermeulen D and Suetens P.Comparative evaluation of multiresolution optimization strategies for multimodality image registration by maximization of mutual information[J].Medical Image Analysis,1999,3(4):373-386.
[44]Chen L,Yap K H.An effective technique for subpixel image registration under noisy conditions[J].IEEE Transactions on System,Man,and Cybernetics-Part A:Systems and humans,2008,38(4):881-887.
[45]Robinson D,Milanfar P.Fundamental performance limits in image registration[J]. IEEE Transactions on Image Processing,2004,13(9):1185-1199.
[46]Anderson JMM,Giannakis GB.Image motion estimation algorithms using cumulants[J].IEEE Transactions on Image Processing,1995,4(3):346-357.
[47]沈民奋,孙丽莎.现代随机信号与系统分析[M].北京:科学出版社,1998.161-175.
[48]张贤达.现代信号处理,第二版[M].北京:清华大学出版社,2002.263-274
[49]Penney G P,Weese J,Little J A,et al.A comparison of similarity measures for use in 2D-3D medical image registration[J].IEEE Transactions on Medical Imaging,1998,17(4):586-595.
[50]http://www.nlm.nih.gov/research/visible/getting_data.html
[51]Yang J,Wang YT,Tang SY,et al.Multiresolution elastic registration of X-ray angiography images using thin-plate spline[J].IEEE Transactions on Nuclear Science,2005,54(1):152-166.
[52]Cao ZG.Liu XX,Peng B,et al.DSA image registration based on multiscale gabor filters and mutual Information[A].In:Proceedings of the 2005 IEEE International Conference on Information Acquisition[C],Hong Kong & Macau,2005:105-110.
[53]Ojansivu V,Heikkil(a|¨) J.Image registration using blur-invariant phase correlation [J].IEEE Signal Processing Letters,2007,14(7):449-452.
[2]Maintz JBA and Viergever MA.A survey of medical image registration[J].Medical Image Analysis.1998,2(1):l-36.
[3]Pluim JPW,Maintz JBA,Viergever MA.Mutual-information-based registration of medical images:A survey [J].IEEE Transactions on Medical Imaging,2003,22(8):986-1004.
[4]Maes F,Collignon A,Vandermeulen D,et al.Multimodality image registration by maximization of mutual information [J].IEEE Transactions on Medical Imaging,1997,16(2):187-198.
[5]Maes F,Vandermeulen D,Suetens P.Medical image registration using mutual information [J].Proceeding of the IEEE,2003,91(10):1699-1722.
[6]Jin J,Wang Q,Shen Y.Registering multiple medical images using the shared chain mutual information[J].Chinese Optics Letters.2007,5(7):389-392.
[7]Meijering EHW,Niessen WJ,Viergever MA.Retrospective motion correction in digital subtraction angiography:a review [J].IEEE Transactions on Medical Imaging,1999,18(1):2-21.
[8]Bentoutou Y,Taleb N.Automatic extraction of control points for digital subtraction angiography image enhancement [J].IEEE Transactions on Nuclear Science,2005,52(1):238-246.
[9]Bookstein FL.Principal warps:thin-plate splines and the decomposition of deformations [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1989,11(6):567-585.
[10]Wan R,Li ML.An Overview of Medical Image Registration[A].In:Proceedings of the Fifth International Conference on Computational Intelligence and Multimedia Applications[C],2003.
[11]http://www.bic.mni.mcgill.ca/brainweb
[12]朱翠玲.现代医学影响学[M].山东科学技术出版社,2000
[13]Brown LG.A survey of image registration techniques[J].ACM Computing Surveys,1992,24(4):326-376.
[14]Viola P and Wells WM.Alignment by maximization of mutual information[J].In:Proceedings of the 5th International Conference on Computer Vision,1995:16-23
[15]Studholme C,Hill DLG,and Hawkes DJ.An overlap invariant entropy measure of 3D medical image alignment[J].Pattern Recognition,1999,32(1):71-86.
[16]Pluim JPW,Maintz JBA,Viergever MA.Image registration by maximization of combined mutual information and gradient information[J].IEEE Transactions on Medical Imaging,2000,19(8):809-814.
[17]Maes F,Collignon A,Vandermeulen D,Marchal G,Suetens P.Multimodality image registration by maximization of mutual information[J].IEEE Transactions on Medical Imaging,1997,16:187-198.
[18]Sawhney HS,Kumar R.True multi-image alignment and its applications to mosaicing and lens distortion correction[J].IEEE Transactions on Pattern Analysis and Machine Intellingece,2001,21:235-243.
[19]Radau PE,Slomka PJ,Julin P,Svensson L and Wahlund LO.Evaluation of linear registration algorithms for brain SPECT and the errors due to hypoperfusion lesions[J].Medical Physics,2001,28(8):1660-1668.
[20]Studholme C,Novotny E,Zubal IG,Duncan JS.Estimating tissue deformation between functional images induced by intracranial electrode implantation using anatomical MRI[J].NeuroImage,2001,13(4):561-576.
[21]陈国良,王煦法,庄镇泉,等。遗传算法及其应用[M].北京:人民邮电出版社,1996.
[22]Nikou C,Heitz F,and Armspach JP.Robust voxel similarity metrics for the registration of dissimilar single and multimodal images[J].Pattern Recognition,1999,32(8):1351-1368.
[23]冈萨雷斯.数字图像处理[M],第二版.北京:电子工业出版社,2007.176-183
[24]Djamdji JP,Bajaoui A,Maniere R.Geometrical registration of images:the multiresolution approach[J].Photogrammetric Engineering and Remote Sensing,1993,53:645-653.
[25]Allen RL,Kamangar FA,and Stokely EM.Laplacian and Orthogonal Wavelet Pyramid Decompositions in Coarse-to-Fine Registration[J].IEEE Transactions on Signal Processing,1993,41(12):3536-3541.
[26]Lemieux L,Jagoe R.Effect of fiducial Marker localization on stereotactic target coordinate calculation in CT slices and radiographs[J].J.Of Physics in Medicine and Biology,1994,39:1915-1928.
[27]Savi A,Gilardi MC,Rizzo G,Pepi M,Landoni C,Ross C.Spatial registration of echocardiographic and positron emission tomographic heart studies[J].European Journal of Nuclear Medicine.1995,22(3):243-247.
[28]Chen G.TY,Pelizzari CA.Image correlation techniques in radiation therapy planing[J].Computerized Medical Imaging and Graphics.1989,13:235-240.
[29]梅跃松,杨树兴,莫波.基于Canny算子的改进的图像边缘检测方法[J].激光与红外,2006,36(3):501-503.
[30]严国萍,何俊峰.高斯.拉普拉斯边缘检测算子的扩展研究[J].华中科技大学学报,2006,34(10):21-23.
[31]Alpert NM,Bradshaw JF,Kennedy D,Correia JA.The principle axis transformation-a method for image registration[J].J.of Nuclear medicine.1990,31:1717-1722.
[32]Colligono A,Vandermeulen D,Suetens P,Marchal G 3D multimodality medical image registration using feature space clustering[J].Computer Vision,Virtual Reality and Robotics in Medicine,Lecture Notes in Computer Science,1995,905:195-204.
[33]Studholme C,Hill DLG,Hawkes DJ.Multiresolution voxel similarity measures for MR-PET registration[A].In:Proceedings of Informaion Processing in Medical Imaging[C],1995:287-298.
[34]Viola P and Wells WM.Alignment by maximization of mutual information[A].In:Proceedings of the 5th International Conference on Computer Vision[C], 1995:16-23.
[35]Pennee X,Cachier P,Ayache N.Tracking brain deformations in time sequences of 3D US images[J].Pattern Recognition Letters,2003,24:801-823.
[36]Boes JL,Meyer CR.Multi-variate mutual information for registration[J].Medical Image Computing and Computer-Assisted Intervention,Lecture Notes in Computer Science,1999,1678:606-612.
[37]Jin J,Wang Q,Shen Y.Nonlinear correlation measurement based registration for sequential images[A].In:Proceedings of IEEE Instrumentation and Measurement Technology Conference[C],Warsaw,Poland:2007:1-5.
[38]Studholme C,Hill DLG,Hawkes DJ.Incorporating connected region labeling into automated image registration using mutual information[J].Mathematical Methods in Biomedical Image Analysis.1996:23-31.
[39]Wang B,Shen Y.A method on calculating high-dimensional mutual information and its application to registration of multiple ultrasound images[J].Ultrasonics (2006),doi:10.1016/j.ultras.2006.07.012
[40]王海南,郝重阳,雷方元,张先勇.非刚性医学图像配准研究综述[J].计算机工程与应用.2005.11.180-184.
[41]Kern JP and Pattichis MS.Robust multispectral image registration using mutual-information models[J].IEEE Transactions on Geoscience and Remote Sensing,2007,45(5):1494-1504.
[42]Rosenfeld A and Kak AC.Digital Pictrure Processing Vol Ⅰ and Ⅱ[M].Academic Press,Orlando,FL,1982.
[43]Maes F,Vandermeulen D and Suetens P.Comparative evaluation of multiresolution optimization strategies for multimodality image registration by maximization of mutual information[J].Medical Image Analysis,1999,3(4):373-386.
[44]Chen L,Yap K H.An effective technique for subpixel image registration under noisy conditions[J].IEEE Transactions on System,Man,and Cybernetics-Part A:Systems and humans,2008,38(4):881-887.
[45]Robinson D,Milanfar P.Fundamental performance limits in image registration[J]. IEEE Transactions on Image Processing,2004,13(9):1185-1199.
[46]Anderson JMM,Giannakis GB.Image motion estimation algorithms using cumulants[J].IEEE Transactions on Image Processing,1995,4(3):346-357.
[47]沈民奋,孙丽莎.现代随机信号与系统分析[M].北京:科学出版社,1998.161-175.
[48]张贤达.现代信号处理,第二版[M].北京:清华大学出版社,2002.263-274
[49]Penney G P,Weese J,Little J A,et al.A comparison of similarity measures for use in 2D-3D medical image registration[J].IEEE Transactions on Medical Imaging,1998,17(4):586-595.
[50]http://www.nlm.nih.gov/research/visible/getting_data.html
[51]Yang J,Wang YT,Tang SY,et al.Multiresolution elastic registration of X-ray angiography images using thin-plate spline[J].IEEE Transactions on Nuclear Science,2005,54(1):152-166.
[52]Cao ZG.Liu XX,Peng B,et al.DSA image registration based on multiscale gabor filters and mutual Information[A].In:Proceedings of the 2005 IEEE International Conference on Information Acquisition[C],Hong Kong & Macau,2005:105-110.
[53]Ojansivu V,Heikkil(a|¨) J.Image registration using blur-invariant phase correlation [J].IEEE Signal Processing Letters,2007,14(7):449-452.