基于定量定性互信息的医学图像配准
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摘要
综合利用同一患者在不同时期或从不同成像设备获得的图像信息是医学图像分析的一个基本任务。为了对多幅图像所提供的信息进行整合,首先要解决多幅图像之间的匹配问题,即图像配准问题。医学图像配准是指对一幅医学图像寻求一种(或一系列)空间变换,使它与另一幅图像上的对应点达到空间位置和解剖位置上的完全一致。配准的结果应使两幅图像中所有解剖点、或至少是所有具有诊断意义上的点都达到匹配。近年来,研究人员提出了许多不同的配准方法。其中,应用最为广泛的方法当属基于互信息的配准方法。
目前,所有基于互信息的配准方法在计算两幅图像的互信息时均假设图像中的像素是独立同分布的。但事实上,不同的像素在图像中的重要性以及它们对图像配准的效用是不同的。独特的像素在图像配准中往往具有更高的效用,所以在决定两幅图像的变换过程中应该起更大的作用。例如,由于匹配靠近大脑皮层的白质点比匹配位于大面积白质区域内的白质点更为有效,因此靠近大脑皮层的白质点在计算两幅脑图像的互信息时应该贡献更多的作用。
为了在图像配准过程中结合像素的效用,本文首先从控制论的角度出发,提出了一种新的信息测度-定量定性互信息。然后,提出了基于定量定性互信息的配准方法。为了定义两幅图像的定量定性互信息,本文提出使用独特性值来表示像素在图像中的重要性以及它相对于图像配准的效用,并通过综合两幅图像像素的效用来定义图像亮度对的联合效用。实验结果表明:与基于传统互信息的配准方法相比,基于定量定性互信息的配准方法极大的提高了配准算法的成功率(成功率的提高量约为20%),从而显示了所提出方法的鲁棒性。
为了确保配准方法的精确性,本文又提出了基于定量定性互信息的层次化的配准方法。在层次化的配准方法中,像素的效用不再是固定不变的,而是随着配准的进展而不断地变化,并在配准的最后阶段,使所有的像素对配准起相同的贡献作用。即,在配准的初始阶段,像素的初始效用由独特性值决定;随着图像配准的进展,像素的效用逐渐变为1。于是,通过在配准的初始阶段依靠具有较高效用的像素或区域,配准方法的鲁棒性得到了提高;通过在配准的最后阶段将像素的效用逐渐变为1,配准方法得到了与基于传统互信息的配准方法类似的配准精度。
在本文中,基于定量定性互信息的层次化的配准方法被应用于3D临床数据(例如,MR,CT和PET)的刚体配准中。实验结果表明:与传统互信息产生的配准函数相比,定量定性互信息产生的配准函数不仅更为光滑,而且还拥有更大的收敛范围。同时,实验结果还表明:层次化的配准方法不仅提高了配准方法的鲁棒性,还使配准方法拥有可达到子体素精度的配准结果。
在许多临床应用中,刚体变换并不足以描述图像间的变形,这时就需要考虑非刚体配准,因此本文还研究了一般化的基于定量定性互信息的非刚体配准方法。另外,为了节省计算时间,我们还推导了定量定性互信息相对于变换参数的梯度的解析形式,从而使得配准算法可以采用基于梯度的优化方法。基于定量定性互信息的非刚体配准方法被应用于MR breast序列图像的运动校正。实验结果表明:与层次化的刚体配准方法相比,基于定量定性互信息的非刚体配准方法可以有效地减少由breast运动引起的图像差异,从而得到更好的配准结果。
A fundamental problem in medical image analysis is the integration of information from multiple images of the same subject, acquired using the same or different modalities and possibly at different time. In order to fuse the information from different images, an essential problem, which should be solved firstly, is to align one image to the other images. Medical image registration is to find the geometric relationship between corresponding points in different images. After image registration, all anatomical points and other points of interest in the images should be easily related. Various registration methods have been proposed over recent years. Among them, registration strategy based on maximization of mutual information (MI) has been proved to be a promising method and has been widely used in medical image registration.
However, almost all mutual information based registration methods treat the voxels of the images equally, when calculating their mutual information. In fact, different voxels have different characteristic and utilities on image registration. Salient voxels should have higher utility, and hence contribute more to determine the transformation between two images. For example, when measuring the mutual information of two brain images, the white matter (WM) voxels near the cortex should contribute more than the WM voxels inside the large WM regions since it is more effective to match WM voxels near cortex than the inside regions.
To incorporate utility information into the image registration procedure, we propose a new information measure, named quantative-qualtitative measure of mutual information (Q-MI), in the view of cybernetics. Then, we propose an image registration method based on Q-MI. To define the Q-MI of two images, we use salient values to represent voxels’s significance in the image and also utility in image registration. Moreover, the joint utilities of intensity pairs are calculated from integrating the voxels utilities in the two images. In order to test the performance of the proposed method, we design lots of quantitative experiments using simulate brain images. Experimental results show that compared to MI-based registration method, Q-MI-based registration method has a higher successful rate and the increased rate can achieve more than 20 percent, which indicates the robustness of the proposed method.
To assure that the registration method has high accuracy, we propose a hierarchical registration strategy based on Q-MI. In the hierarchical registration strategy, the utility values of voxels are not fixed, and they will be hierarchically updated during the registration procedure, with all voxels contributing equally in the final stage. In particular, the initial utility of each voxel will assigned according to its saliency value; with the progress of image registration, this utility will gradually move towards to one. Thus, by mainly focusing on the voxels (or the regions) with higher utilities in the initial registration procedure, the robustness of registration can be improved. Also, by changing each joint utility to one in the final stage, the sub-voxel accuracy of registration can be retained as that obtained by the conventional MI-based registration methods, because of using MI in the final registration procedure.
In this paper, the proposed Q-MI has been validated and applied to the rigid registrations of clinical brain images, such as MR, CT and PET images. Experimental results demonstrate that the registration function generated by Q-MI is much smoother than that by MI, and it has a larger capture range due to the incorporation of the joint utilities of the two images into the Q-MI measurement. Moreover, experimental results also show that hierarchical registration strategy not only improves the robustness of registration method, but also makes it have sub-voxel accuracy.
In many applications, a rigid transformation is not sufficient to describe the deformation between two images, thus nonrigid transformations are required. In this thesis, we studied a general nonrigid registration method based on quantitative-qualitative measure of mutual information. In addition, we also derive the analytic expression for the gradient of Q-MI w.r.t the transformation parameter when partial volume interpolation is used. Therefore, the registration strategy can hire gradient-based optimization method. We applied the proposed method to correct the motion between MR breast images.
Experimental results show that the proposed method performed well and can reduce effectively the difference casued by the motion of breast.
目前,所有基于互信息的配准方法在计算两幅图像的互信息时均假设图像中的像素是独立同分布的。但事实上,不同的像素在图像中的重要性以及它们对图像配准的效用是不同的。独特的像素在图像配准中往往具有更高的效用,所以在决定两幅图像的变换过程中应该起更大的作用。例如,由于匹配靠近大脑皮层的白质点比匹配位于大面积白质区域内的白质点更为有效,因此靠近大脑皮层的白质点在计算两幅脑图像的互信息时应该贡献更多的作用。
为了在图像配准过程中结合像素的效用,本文首先从控制论的角度出发,提出了一种新的信息测度-定量定性互信息。然后,提出了基于定量定性互信息的配准方法。为了定义两幅图像的定量定性互信息,本文提出使用独特性值来表示像素在图像中的重要性以及它相对于图像配准的效用,并通过综合两幅图像像素的效用来定义图像亮度对的联合效用。实验结果表明:与基于传统互信息的配准方法相比,基于定量定性互信息的配准方法极大的提高了配准算法的成功率(成功率的提高量约为20%),从而显示了所提出方法的鲁棒性。
为了确保配准方法的精确性,本文又提出了基于定量定性互信息的层次化的配准方法。在层次化的配准方法中,像素的效用不再是固定不变的,而是随着配准的进展而不断地变化,并在配准的最后阶段,使所有的像素对配准起相同的贡献作用。即,在配准的初始阶段,像素的初始效用由独特性值决定;随着图像配准的进展,像素的效用逐渐变为1。于是,通过在配准的初始阶段依靠具有较高效用的像素或区域,配准方法的鲁棒性得到了提高;通过在配准的最后阶段将像素的效用逐渐变为1,配准方法得到了与基于传统互信息的配准方法类似的配准精度。
在本文中,基于定量定性互信息的层次化的配准方法被应用于3D临床数据(例如,MR,CT和PET)的刚体配准中。实验结果表明:与传统互信息产生的配准函数相比,定量定性互信息产生的配准函数不仅更为光滑,而且还拥有更大的收敛范围。同时,实验结果还表明:层次化的配准方法不仅提高了配准方法的鲁棒性,还使配准方法拥有可达到子体素精度的配准结果。
在许多临床应用中,刚体变换并不足以描述图像间的变形,这时就需要考虑非刚体配准,因此本文还研究了一般化的基于定量定性互信息的非刚体配准方法。另外,为了节省计算时间,我们还推导了定量定性互信息相对于变换参数的梯度的解析形式,从而使得配准算法可以采用基于梯度的优化方法。基于定量定性互信息的非刚体配准方法被应用于MR breast序列图像的运动校正。实验结果表明:与层次化的刚体配准方法相比,基于定量定性互信息的非刚体配准方法可以有效地减少由breast运动引起的图像差异,从而得到更好的配准结果。
A fundamental problem in medical image analysis is the integration of information from multiple images of the same subject, acquired using the same or different modalities and possibly at different time. In order to fuse the information from different images, an essential problem, which should be solved firstly, is to align one image to the other images. Medical image registration is to find the geometric relationship between corresponding points in different images. After image registration, all anatomical points and other points of interest in the images should be easily related. Various registration methods have been proposed over recent years. Among them, registration strategy based on maximization of mutual information (MI) has been proved to be a promising method and has been widely used in medical image registration.
However, almost all mutual information based registration methods treat the voxels of the images equally, when calculating their mutual information. In fact, different voxels have different characteristic and utilities on image registration. Salient voxels should have higher utility, and hence contribute more to determine the transformation between two images. For example, when measuring the mutual information of two brain images, the white matter (WM) voxels near the cortex should contribute more than the WM voxels inside the large WM regions since it is more effective to match WM voxels near cortex than the inside regions.
To incorporate utility information into the image registration procedure, we propose a new information measure, named quantative-qualtitative measure of mutual information (Q-MI), in the view of cybernetics. Then, we propose an image registration method based on Q-MI. To define the Q-MI of two images, we use salient values to represent voxels’s significance in the image and also utility in image registration. Moreover, the joint utilities of intensity pairs are calculated from integrating the voxels utilities in the two images. In order to test the performance of the proposed method, we design lots of quantitative experiments using simulate brain images. Experimental results show that compared to MI-based registration method, Q-MI-based registration method has a higher successful rate and the increased rate can achieve more than 20 percent, which indicates the robustness of the proposed method.
To assure that the registration method has high accuracy, we propose a hierarchical registration strategy based on Q-MI. In the hierarchical registration strategy, the utility values of voxels are not fixed, and they will be hierarchically updated during the registration procedure, with all voxels contributing equally in the final stage. In particular, the initial utility of each voxel will assigned according to its saliency value; with the progress of image registration, this utility will gradually move towards to one. Thus, by mainly focusing on the voxels (or the regions) with higher utilities in the initial registration procedure, the robustness of registration can be improved. Also, by changing each joint utility to one in the final stage, the sub-voxel accuracy of registration can be retained as that obtained by the conventional MI-based registration methods, because of using MI in the final registration procedure.
In this paper, the proposed Q-MI has been validated and applied to the rigid registrations of clinical brain images, such as MR, CT and PET images. Experimental results demonstrate that the registration function generated by Q-MI is much smoother than that by MI, and it has a larger capture range due to the incorporation of the joint utilities of the two images into the Q-MI measurement. Moreover, experimental results also show that hierarchical registration strategy not only improves the robustness of registration method, but also makes it have sub-voxel accuracy.
In many applications, a rigid transformation is not sufficient to describe the deformation between two images, thus nonrigid transformations are required. In this thesis, we studied a general nonrigid registration method based on quantitative-qualitative measure of mutual information. In addition, we also derive the analytic expression for the gradient of Q-MI w.r.t the transformation parameter when partial volume interpolation is used. Therefore, the registration strategy can hire gradient-based optimization method. We applied the proposed method to correct the motion between MR breast images.
Experimental results show that the proposed method performed well and can reduce effectively the difference casued by the motion of breast.
引文
[1] 康晓东. 现代医学影像技术. 天津:天津科学翻译出版公司. 2001.
[2] 罗述谦. 医学图像配准技术. 国外医学生物医学工程分册. 1999, 22(1):1-7.
[3] Maintz, J. B. A., Max A. Viergever. A survey of medical image registration [J]. Medical Image analysis. 1998, 2(1):1-36.
[4] Hill, D. L. G., Batchelor, P. G.., Holden, M., Hawkes, D. J. Medical image registration [J]. Physics In Medicine and Biology. 2001, 46:1-45.
[5] Zitova, B., Flusser, J. Image registration methods: a survey [J]. Image and Vision Computing. 2003, 21:977-1000.
[6] Verbeeck, R. Rinciples and practive of a workstation for the pre-operative planning of stereotactic neurosurgical interventions: from implementation to clinical evaluation [Ph.D. thesis]. Belgium: Leuven, 1996.
[7] Gildenberg, P. L., Tasker, R. R. Textbook of stereotactic and functional neurosurgery. New York, NY: McGraw-Hill. 1998.
[8] Simon, D. A., O’Toole, R. V., Blackwell, M., Morgan, F., Digioia, A. M., Kanade, T. Accuracy validation in image-guided orthopaedic surgery [J]. In Medical Robotics and Computer Assisted Surgery. 1995, pp.185-192.
[9] Maurer, C. R., Fitzpartick, J. M., Galloway, R. L., Wang, M. Y., Maciunas, R. J., Allen, G. S. The accuracy of image-guided neurosurgery using implantable fiduical markers [J]. Computer Assosted Radiology. 1995, pp.1197-1202.
[10] Ellis, R. E., Toksvig-Larsen, S., Marcacci, M., Caramella, D., Fadda, M. A biocompatible fiduical marker for evaluating the accuracy of CT image registration [J]. Computer Assisted Radiology. 1996, 1124:693-698.
[11] Biswal, B. B., Hyde, J. S. Contour-based registration technique to differentitate between task-activated and head motion-induced calibration for freehand 3D ultrasound. Medical Image Computing and Computer-Assisted Intervention. 2000, 1935:462-471.
[12] Declerck, J., Feldmar, J., Goris, M. L., Betting, F. Automatic registration and alignment on a templated of cardiac stress and rest reoriented SPECT images [J]. IEEE Transactions on MedicalImaging. 1997, 16(6):727-737.
[13] Gilhuijs, K. G. A., Herk, M. van. Automatic on-line inspection of patient setup in radiation therapy using digital portal images [J]. Medical Physics. 1993, 20(3):667-677.
[14] Grimson, W. E. L., Ettinger, G. J., White, S. J., Lozano-Perez, T., Wells, W. M., Kikinis, R. An automation registration method for frameless stereotaxy, image guided surgery, and enhanced reality visualization [J]. IEEE Transactions on Medical Imaging. 1996, 15(2):129-140.
[15] Grzeszczuk, R., Tan, K. K.., Levin, D. N., Pelizzari, C. A., Hu, X., Chen, G. T. Y., Beck, R. N., Chen, C. T., Cooper, M., Milton, J., Spire, J. P., Towle, V. L., Dohrmann, G. J., Erickson, R. K. Retrospective fusion of radiographic and MR data for localization of subdural electrodes [J]. Journal of Computer Assisted Tomography. 1992, 16(5):7 64-773.
[16] Herring, J. L., Dawant, B. M., Maurer, C. R., Muratore, D. M., Galloway, R. L., Fitzpatrick, J. M. Surfact-based registration of CT images to physical space for image-guided surgery of the spine: a sensitivity study [J]. IEEE Transaction on Medical Imaging. 1998, 17(5):743-761.
[17] Szeliski, R., Lavallee, S. Matching 3-D anatomical surfaces with non-rigid deformations using octree-splines [J]. International Journal of Computer Vision. 1996, 18(2):171-186.
[18] Thirion, J. –P. Image matching as a diffusion process: an analogy with Maxwell’s demons [J]. Medical Image Analysis. 1998, 2(3):243-260.
[19] Bajcsy, R., Kovacic, S. Multiresolution elastic matching [J]. Computer Vision, Graphics and Image Processing. 1989, 46:1-21.
[20] Cachier, P., Pennec, X. 3D non-rigid registration by gradient descent on a Gaussian-windowed similarity measure using convolutions. In Matchematical Methods in Biomedical Image Analysis, IEEE Computer Society Press, Los Alamitos, CA. 2000, pp.182-189.
[21] Gee, J. C., Alsop, D. C., Aguirre, G. K. Effect of spatial normalization on alalysis of functional data. Proceeding of SPIE in Medical Imaging: Image Processsing. 1997, 3034:550-560.
[22] Lemieux, L., Wieshmann, U. C., Moran, N. F., Fish, D. R., Shorvon, S. D. The detection and significance of subtle change in mixed-signal brain lesions by serial MRI scan matching and spatial normalization [J]. Medical Image analysis. 1998, 2(3):227-242.
[23] Christensen, G. E., Joshi, S. C, Miller, M. I. Volumetric transformation of brain anatomy [J]. IEEE Transaction on Medical Imaging. 1997, 16(6):864-877.
[24] Gee, J. C., Haynor, D. R., Briquer, L. Le., Bajcsy, R. K. Advances in elastic matching theory and its implementation. Proceeding s of Computer Vision, Virtual Reality and Robotics in Medicine and Medical Robotics and Computer-Assisted Surgery. 1997, 1205:63-72.
[25] Ashburner, J., Friston, K. J. Nonlinear spatial normalization using basis functions [J]. Human Brain Mapping. 1999, 7:254-266.
[26] Woods, R. P., Cherry, S. R., Mazziotta, J. C. Rapid automated algorithm for aligning and reslicing PET images [J]. Journal of Computer Assisted Tomography. 1992, 16:620-633.
[27] Woods, R. P., Mazziotta, J. C., Cherry, S. R. MRI-PET registration with automated algorithm [J]. Journal of Computer Assisted Tomography. 1993, 17(4):536-546.
[28] Hill, D. L. G., Hawkes, D. J., Harrison, N. A., Ruff, C. F. A strategy for automated multimodality image registration incorporating anatomical knowledge and imager characteristics. Information Processing in Medical Imaging. 1993, 687:182-196.
[29] Collignon, A. Multi-modality medical image registration by maximization of mutual information [Ph.D. thesis]. Leuven: Catholic University of Leuven. 1998.
[30] Collignon, A., Maes, F., Delaere, D., Vandermeulen, D, Suetens, P., Marchal, G. Automated multi-modality image registration based on information theory. Information Processing in Medical Imaging. 1995, pp.263-274.
[31] Viola, P. A., Wells III, W. M. Alignment by maximization of mutual information. International Conference on Computer Vision, IEEE Computer Society Press. 1995, pp.16-23.
[32] Viola, P. A. Alignment by maximization of mutual information [Ph.D. thesis]. Boston: Massachusetts Institute of Technology. 1995.
[33] Wells III, W. M., Viola, R., Kikinis, R. Multi-modal volume registration by maximization of mutual information. Medical Robotics and Computer Assisted Surgery. 1995, pp.55-62.
[34] Studholme, C., Hill, D.L.G., Hawkes, D.J. Multiresolution voxel similarity measures for MR-PET registration. Proceedings of the International Conference on Information Processing in Medical Imaging (IPMI). 1995, 3:287-298.
[35] Studholme, C., Hill, D.L.G., Hawkes, D.J. Automated 3-D registration of MR and CT images of the head [J]. Medical Image Analysis. 1996, 1(2):163-175.
[36] Studholme, C., Hill, D.L.G., Hawkes, D.J. Automated three-dimensional registration of magnetic resonance and positron emission tomography brain images by multiresolution optimization of voxel similarity measure. Medical Physics. 1996, 24(1):25-35.
[37] Wells III, W. M., Viola, Atsumi, H., Nakajima, S., Kikinis, R. Multi-modal volume registration by maximization of mutual information. Medical Image Analysis. 1996, 1:35-51.
[38] Meyer, C. R., Boes, L. J., Kim, B., Bland, P. H., Zasadny, K. R., Kison, P. V., Koral, K., Frey, K. A., Wahl, R. L. Demonstration of accuracy and clinical versatility of mutual information for automaticmultimodality image fusion using affine and thin-plate spline warped geometric deformations [J]. Medical Image Analysis. 1996, 1(3):195-206.
[39] Maes, F., Vandermeulen, D., Suetens, P. Medical image registration using mutual information [J]. Proceedings of the IEEE. 2003, 91:1699-1722.
[40] Belis, M., Guiasu, S. A quantitative-qualitative measure of information in cybernetic systems [J]. IEEE Trans. Inform. Theory. 1968, 14:593-594.
[41] Taneja, H. C., Tuteja, R. K. Characterization of a quantitative-qualitative measure of relative information [J]. Information Sciences. 1984, 33:217-222.
[42] Taneja, H. C. On the quantitative-qualitative measure of relative information [J]. Information Sciences. 1984, 33:223-227.
[43] Kadir, T., Brady, M. Saliency, scale and image descriptions [J]. International Journal of Computer Vision. 2001, 45:83-105.
[44] Kadir, T. Scale, saliency and Scene description [Ph.D. thesis]. Oxford: University of Oxford. 2002.
[45] Huang, X., Sun, Y., Metaxas, D., Sauer, F., Xu, C. Hybrid image registration based on configural matching of scale-invariant salient region features. Proceedings of the Conference on Computer Vision and Pattern Recognition Workshop. 2004, pp.167.
[46] Lao, Z., Shen, D., Jawad, A., Karacali, B., Liu, D., Melhem, E., Bryan, N., Davatzikos, C. Automated segmentation of white matter lesions in 3D brain MR images using multivariate pattern classification. Proceedings of the 3rd IEEE International Symposium on Biomedical Imaging (ISBI). 2006, pp.307-310.
[47] Hata, N., Dohi, T., Warfield, S. K., Wells, W. M., Kikinis, R., Jolesz, F.A. Multimodality deformable registration of pre- and intraoperative images for MRI-guided brain surgery. Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). 1998, pp 1067-1074.
[48] Bharatha, A., Hirose, M., Hata, N., Warfield, S. K., Ferrant, M., Zou, K. H., Suarez-Santana, E., Ruiz-Alzola, J., D'Amico, A., Cormack, R. A., Kikinis, R., Jolesz, F.A., Tempany, C. M. C. Evaluation of three-dimensional finite element-based deformable registration of pre- and intraoperative prostate imaging. Medical Physics. 2001, 28(12): 2551-2560.
[49] Toga, A. W., Thompson, P. The role of image registration in brain mapping [J]. Image and Vision Computing. 2001, 19(1-2): 3-24.
[50] Pluim, J. P. W., Maintz, J. B., Viergever, M. A. Mutual-information-based registration of medical images: a survey [J]. IEEE Transaction Medical Imaging. 2003, 22:986-1004.
[51] Pettey, D. J., Gee, J. C. Using a linear diagnostic function and non-rigid registration to search for morphological differences between populations: an example involving the male and female corpus callosum. Proceedings of the International Conference on Information Processing in Medical Imaging (IPMI). 2001, pp. 372-379.
[52] Pompe, B., Blidh, P., Hoyer, D., Eiselt, M. Using mutual information to measure coupling in the cardiorespiratory system [J]. IEEE Engineering in Medicine and Biology Magazine. 1998, 17(6): 32-39.
[53] Holden, M., Hill, D. L. G., Jarosz, J. M., Cox, T. C. S., Rohlfing, T., Goodey, J., Hawkes, D. J. Voxel similarity measures for 3-D serial MR brain image registration [J]. IEEE Transaction on Medical Imaging. 2000, 19:94-102.
[54] Freire, L., Roche, A., Mangin, J. –F. What is the best similarity measure for motion correction in fMRI a time series [J]? IEEE Transaction on Medical Imaging. 2002, 21:470-484.
[55] Radau, P. E., Slomka, P. J., Julin, P., Svensson, L., Wahlund, L. –o. Automated segmentation and registration technique for hmpao-spect imaging of Alzheimer’s patients. Proceedings of SPIE on Medical Imaging: Image Processing. 2000, 3979:372-384.
[56] Fei, B., Wheaton, A., Lee, Z., Duerk, J. L., Wilson, D. L. Automatic MR volume registration and its evaluation for the pelvis and prostate [J]. Physics in Medicnine and Biology. 2002, 47(5):823-838.
[57] Slomka, P. J., Mandel, J., Downey, D., Fenster, A. Evaluation of voxel-based registration of 3-D power Doppler ultrasound and 3-D magnetic resonance angiographic images of carotid arteries [J]. Ultrasound in Medicine and Biology. 2001, 27(7):945-955.
[58] Carrillo, A., Duerk, J. L., Lewin, J. S., Wilson, D. L. Semi-automic 3-D image registration as applied to interventional MRI liver cancer treatment [J]. IEEE Transactions on Medical Imaging. 2000, 19(3):175-185.
[59] Holden, M., Denton, E. R. E., Jarosz, J. M., Cox, T. C. S., Studholme, C., Hawkes, D. J., Hill, D. L. G. Detecting small anatomical changes with 3D serial MR subtraction images. Proceedings of SPIE in Medical imaging: Image Processing. 1999, 3661:44-55.
[60] Holden, M., Hill, D. L. G., Denton, E. R. E., Jarosz, J. M., Cox, T. C. S., Rohlfing, T., Goodey, J., Hawkes, D. J. Voxel similarity measures for 3-D serial MR brain iamge registration [J]. IEEE Transactions on Medical Imaging. 2000, 19(2):94-102.
[61] Studholme, C., Cardenas, V., Weiner, M. Multi scale image and multi scale deformation of brain anatomy for building average brain atlases. Proceedings of SPIE in Medical Imaging: Image Processing. 2001, 4322:557-568.
[62] Studholme, C., Novotny, E, Zubal, I. G., Duncan, J. S. Estimating tissue deformation between functional images induced by intracranial electrode implantation using anatomical MRI [J]. NeuroImage. 2001, 13(4):561-576.
[63] Shekhar, K., Zagrodsky, V. Mutual information-based rigid and nonrigid registration of ultrasound volumes [J]. IEEE Transactions on Medical Imaging. 2002, 21(1):1660-1668.
[64] Meyer, C. R., Boes, J. L., Kim, B., Bland, P. H., Lecarpentier, G. L., Fowlkes, J. B., Roubidoux, M. A., Carson, P. L. Semiautomatic registration of volumetric ultrasound scans [J]. Ultrasound in Medicine and Biology. 1999, 25(3):339-347.
[65] Zagrodsky, V., Shekhar, R., Cornhill, J. F. Mutual information based registration of cardiac ultrasound volumes. Proceedings of SPIE in Medical Imaging: Image Processing. 2000, 3979:1605-1614.
[66] Ding, L., Goshtasby, A., Satter, M. Volume image registration by template matching [J]. Image and Vision Computing. 2001, 19(12):821-832.
[67] Holden, M., Schnabel, J. A., Hill, D. L. G. Quantifying small changes in brain ventricular volume using non-rigid registration. Proceedings of Computer Science in Medical Image Computing and Commputer-Assisted Intervention, Lecture Notes. 2001, 2208:49-56.
[68] Studholme, C. Measures of 3D medical image aligment [Ph.D. thesis]. London: University of Londen. 1997.
[69] Zollei, L., Grimson, E., Norbash, A., Wells, W. 2D-3D rigid registration of X-ray fluoroscopy and CT images using mutual information and sparsely samples histogram estimators. Proceedings of IEEE Computer Society in Computer Vision and Pattern Recognition. 2001, 2:696-703.
[70] Hayton, P. M., Brady, M., Smith, S. M., Moore, N. A non-rigid registration algorithm for dynamic breast MR images [J]. Artifical Intelligence. 1999, 114(1-2):125-156.
[71] Plattard, D., Soret, M., Troccaz, J., Vassal, P, Giraud, J. –Y., Champleboux, G.., Artignan, X., Bolla, M. Patient setup using portal images: 2D/2D image resgistration using mutual information [J]. Computer Aided Surgery. 2000, 5(4):246-262.
[72] Studholme, C., Hill, D. L. G., Hawkes, D. J. A normalized entropy measure for multi-modality image alignment. Proceedings of SPIE, Medical Imaging: Image Processing. 1998, 3338: 132-143.
[73] Studholme, C., Hill, D. L. G., Hawkes, D. J. An overlap invarivant entropy measure of 3-D medical image alignment [J]. Pattern Recognition. 1999, 32(1):71-86.
[74] Hill, D. L. G., Maurer, C. R., Maciunas, Jr., R. J., Barwise, J. A., Fitzpatrick, J. M., Wang, M. Y. Measurement of intraoperative brain surface deformation under a craniotomy [J]. Neurosurgery. 1998, 43:514-526.
[75] Likar, B., Pernus, F. A hierarchical approach to elastic registration based on mutual information. Image and Vision Computing. 2001, 19:33-44.
[76] Studholme, C., Constable, R. T., Duncan, J. S. Accurate alignment of functional EPI data to anatomical MRI using a physics-based distortion model [J]. IEEE Transactions on Medical Imaging. 2000, 19:1115-1127.
[77] Rueckert, D., Clarkson, M. J., Hill, D. L. G.., Hawkes, D. J. Non-rigid registration using higher-order mutual information. Proceedings of SPIE, Medical Imaging: Image processing. 2000, 3979:438-447.
[78] Studholme, C., Hill, D. L. G.., Hawkes, D. J. Incorporating connected region labeling into automated image registration using mutual information. Proceedings of the IEEE workshop on Mathematical Methods in Biomedical Image Analysis. 1996, pp. 23-31.
[79] Pluim, J. P. W., Maintz, J. B., Viergever, M. A. Image registration by maximization of combined mutual information and gradient information. IEEE Trans. Medical. Imaging, 2000, 19:809-814.
[80] Rodríguez-Carranza, C. E., Loew, M. H. A weighted and deterministic entropy measure for image registration using mutual information. Proceedings of SPIE, Medical Imaging: Image Processing. 1998, 3338:155-166.
[81] Rodríguez-Carranza, C. E., Loew, M. H. Global optimization of weighted mutual information for multimodality image registration. Proceedings of SPIE, Medical Imaging: Image Processing. 1999, 3338:89-96.
[82] Rui Gan, Albert C. S. Chung. Distance-Intensity for Image Registration. CVBIA. 2005, pp. 281-290.
[83] Rui Gan, Albert C. S. Chung. Multi-dimensional Mutual Information Based Robust Image Registration Using Maximum Distance-Gradient-Magnitude. IPMI. 2005, pp.210-221.
[84] Holden, M., Griffin, L. D., Saeed, N., Hill, D. L. G. Multi-channel mutual information using scale space. Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). 2004, pp.797-804.
[85] Shen, D., Davatzikos, C. HAMMER: Hierarchical attribute matching mechanism for elastic registration [J]. IEEE Trans. on Medical Imaging. 2002, 21(11): 1421-1439.
[86] Hongxia Luan, Feihu Qi, Zhong Xue, Liya Chen, Dinggang Shen. Multimodality image registration by maximization of quantitative-qualitative measure of mutual information [J]. Pattern Recognition. 2007.
[87] Hongxia Luan, Feihu Qi, Dinggang Shen. Multi-model image registration by quantitative-qualitative measure of mutual information (Q-MI). Computer Vision for Biomedical Image applications, ICCV 2005 workshop, October 21, Beijing, China. 2005, pp: 378-387.
[88] Krol, Z., Zerfass, P., Rymon-Lipinski, B., Jansen, T., Hauck, W., Zeilhofer, H. –F., Sader, R., Keeve, E. computer aided osteotomy design for harvesting autologous bone grafts in reconstructive surgery. Proceedings of SPIE, Medical Imaging: Visualization, Image Display, and Image-guided Procedures. 2001, 4319:244-251.
[89] Brinkmann, B. H., O’Brien, D. P., S. Aharon, O’Connor, M. K., Mullan, B. P., Hanson, D. P., Robb, R. A. Quantitative and clinical analysis of SPECT image registration fro epilepsy studies [J]. Journal of Nuclear Medicine. 1999, 40(7):1098-1105.
[90] Koral, K. F., Lin, S., Fessler, J. A., Kaminski, M. S., Wahl, R. L. Prelininary results from intensity-based CT-SPECT fusion in I-131 anti-B1 monoclonal-antibody therapy of lymphoma [J]. Cancer. 1997, 80(12):2538-2544.
[91] Sanjay-gopal, S., Chan, H. P., Wilson, T., Helvie, M., Petrick, N., Saniner, B. A regional registration technique for automated interval change analysis of breast lesions on mammograms [J]. Medical Physics. 1999, 26(12):2669-2679.
[92] Denton, E. R. E., Holden, M., Christ, E., Jarosz, J. M., RussellHones, D., Goodey, J., Cox, T. C. S., Hill, D. L. G. The identification of cerebral volume changes in treated growth hormonedeficient adults using serial 3D MR image processing [J]. Journal of Computer Assisted Tomography. 2000, 24(1):139-145.
[93] Ourselin, S., Roche, A., Prima, S., Ayache, N. Block matching: a general framework to improve robustness of rigid registration of medical images. Proceedings of Lecture Notes in Computer Science, Medical Image Computing and Computer-Assisted Intervention. 2000, 1935:557-566.
[94] Van Laere, K., Koole, M., D’Asseler, Y., Versijpt, J., Audenaert, K., Dumont, F., Dierckx, R. Automated stereotactic standardization of brain SPECT receptor data using single-photon transmission images [J]. Journal of Nuclear Medicine. 2001, 42(2):361-375.
[95] Ritter, N., Owens, R., Cooper, J., Eiklboom, R. H., van Saarloos. P. P. Registration of stereo andtemporal images of the retina [J]. IEEE Transactions on Medical Imaging. 1999, 18(5):404-418.
[96] Jenkinson, M., Smith, S. A global optimization method for robust affine registration of brain images [J]. Medical Image analysis. 2001, 5(2):143-156.
[97] Hill, D. L. G., Maurer, C. R., Jr., Studholme, C., Fitzpatrick, J. M., Hawkes, D. J. Correcting scaling errors in tomographic images using a nine degree of freedom registration algorithm [J]. Journal of Computer Assisted Tomography. 1998, 22(2):317-323.
[98] Holden, M. Registration of 3D serial MR brain images [Ph.D. thesis]. London: University of London. 2001.
[99] Meyer, c., Boes, J., Kim, B., Bland, P. Evaluation of control point selection in automatic, mutual information driven, 3D warping. Proceedings of Lectures Notes in Computer Science. 1998, 1496:944-951.
[100] Horsfield, M. A. Mapping eddy current induced fields for the correction of diffusion-weighted echo planar images [J]. Magnetic Resonance Imaging. 1999, 17(9):1335-1345.
[101] Rueckert, D., Sonoda, L. I., Hayes, C., Hill, D., Leach, M. O., Hawkes, D. J. Nonrigid registration using free-form deformations: application to breast MR images [J]. IEEE Transactions on Medical Imaging. 1999, 18(8):712-721.
[102] Otte, M. Elastic registration of fMRI data using Bezier-spline transformations [J]. IEEE Transactions on Medical Imaging. 2001, 20(3):193-206.
[103] Likar, B., Pernus, F. Registration of serial transverse sections of muscle fibres [J]. Cytometry. 1999, 37(2):93-106.
[104] Hellier, P., Barillot, C. Multimodal non-rigid warping for correction of distortions in function MRI. Proceedings of Lecture Notes in Computer Science, Medical Image Computing and Computer-Assisted Intervention. 2000, 1935:512-520.
[105] Likar, B., Pernus, F. A Hierarchical approach to elastic registration based on mutual information [J]. Image and Vision Computing. 2001, 19(1-2):33-44.
[106] Maintz, J. B. A., Meijering, E. H. W., Viergever, M. A. General multimodal elastic registration based on mutual information. Proceedings of SPIE, Medical Imaging; Image Processing. 1998, 3338:144-154.
[107] Zhu, Y. –M., Cochoff, S. M. Influence of implementation parameters on registration of MR and SPECT brain images by maximization of mutual information [J]. Journal of Nuclear Medicine. 2002, 43(2):160-166.
[108] Thevenaz, P., Unser, M. Optimization of mutual information for multiresolution imageregistration [J]. IEEE Transactions on Image Processing. 2000, 9(12):2083-2099.
[109] Netsch, T., Rosch, P., Weese, J., van Muiswinkel, A., Desmedt, P. Grey value-based 3-D registration of functional MRI time-series: comparison of interplolation order and similarity measure. Proceedings of SPIE, Medical Imaging: Image Processing. 2000, 3979:1148-1159.
[110] F. Maes. Segmentation and registration of multimodal medical Images: from theory, implementation and validation to a useful tool in clinical practice [Ph.D. thesis]. Leuven: Catholic University of Leuven. 1998.
[111] Pluim, J. P. W., Maintz, J. B., Viergever, M. A. Interplation artifacts in mutual information-based image registration [J]. Computer Vision and Image Understanding. 2000, 77:211-232.
[112] Tsao, J. Interpolation artifacts in multimodality image registration based on maximization of mutual information [J]. IEEE Transaction on Medical Imaging. 2003, 22(7):854-965.
[113] Kim, J., Fisher, J. W., Tsai, A., Wible, C., Willsky, A. S., Wells. W. M. Incorporating spatial priors into an information theoretic approach for fMRI data analysis. Proceedings of Lecture Notes in Computer Science, Medical Image Computing and Computer-Assisted Intervention. 2000, 1935:62-71.
[114] Jenkinson, M., Bannister, P., Brady, m., Smith, S. Improved optimization for the robust and accurate linear registration and motion correction of brain images [J]. NeuroImage. 2002, 17:825-841.
[115] Maes, F., Vendermeulen, D., Sutens, 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.
[116] Boes, J. L., Meyer, C. R. Multi-variate mutual information for registration. Proceedings of Lecture Notes in Computer Science, Medical Image Computing and Computer-Assisted Intervention. 1999, 1679:606-612.
[117] Thevenaz. P., Unser, M. A pyramid approach to sub-pixel image fusion based on mutual information. International Conference on Image Processing. 1996, 265-268.
[118] Bansal, R., Staib, L., Chen, Z., Rangarajan, A., Kinsely, J., Nath, R., Duncan, J. A minimax entropy registration framework for patient setup verification in radiotherapy [J]. Computer Aided Surgery. 1999, 4(6):287-304.
[119] Kim, j., Fessler, J. A., Lam, K. L., Balter, J. M., Ten Haken, R. K. A feasibility study on mutual information based set-up estimator for radiotherapy [J]. Medical Physics. 2001,28(12):2507-2517.
[120] Rupin, D., Rafeef, A., Mark, P., Jennie, S., Paul, S. Registration of 2D to 3D joint images using phase-based mutual information. Proceedings of SPIE, Medical Imaging: Image Processing. 2007, 6512:1-9.
[121] Chen, X., Brady, M., Lok-Chuen Lo, J., Moore, N. Simultansous Segmentaion and Registration of Contrast-Enhanced Breast MRI. IPMI. 2005, pp.126-137.
[122] Andresson, J. L. R., Thurfjell, L. A multivariate approach to registration of dissimilar tomographic images [J]. European journal of Nuclear Medicine. 1999, 26(7):718-733.
[123] Cover, T. M., Thomas, J. A. Elements of Information Theory. New York. 1975.
[124] Mastuda, H. Physical nature of higher-order mutual information: intrinsic correlations and frustration [J]. Physical Review E. 2000, 62(3):3096-3102.
[125] Lynch, J. A., Peterfy, C. G., White, D. L., Hawkins, R. A., Genant, H. K. MRI-SPECT image registration using multiple MR pulse sequences to examine osteoarthritis of the knee. Proceedings of SPIE, Medical Imaging: Image Processing. 1999, 3661:68-77
[126] Krucker, J. F., Meyer, C. R., LeCarpentier, g. L., Fowlkes, J. B., Carson, P. L. 3D partial compounding of ultrasound images using image-based nonrigid registration [J] .Ultrasound in Medicine and Biology. 2000, 26(9):1475-1488.
[127] West, J., Fitzpatrick, J. M., Wang, M. Y., Dawant, B. M., Maurer Jr, C. R., Kessler, R. M., Maciunas, R. J., Barillot, C., Lemoine, D., Collignon, A., Maes, F., Suetens, P., Vandermeulen, D., van den Elsen, P. A., Napel, S., Sumanaweera, T. S., Harkness, B., Hemler, P. F., Hill, D. L. G., Hawkes, D. J., Studholme, C., Maintz, J. B., Viergever, M. A., Malandain, G., Woods, R. P. Comparison and evaluation of retrospective intermodality brain image registration techniques [J]. Journal Comput Assist Tomogr. 1997, 21(4):554-566.
[128] Nikou, C., Heitz, F., Armspach, J. –P. Robust voxel similarity metrics for the registration of dissimilar single and multimodal images [J]. Pattern Recognition. 1999, 32(8):1351-1368.
[129] Holmes, D. R., Camp, J., Robb, R. A. Evaluation of search strategies and cost functions in optimizing voxel-based image registration. Proceedings of SPIE, Medical Imaging: Image Display. 1996, 2707:554-562.
[130] Roche, A., Pennec, X., Malandain, G., Ayache, N. Rigid registration of 3-D ultrasound with MR images: a new approach combining intensity and gradient information [J]. IEEE Transactions on Medical Imaging. 2001, 20(10):1038-1049.
[131] Blackall, J. M., Rueckert, D., Maurer, C. R., Jr., Penney, G. P., Hill, D. L. G., Hawkes, D. J. Animage registration approach to automated calibration for freehand 3D ultrasound. Proceedings of Lecture Notes in Computer Science, Medical Image Computing and Computer-Assisted Intervention. 2000, 1935:462-471.
[132] Erdi, Y. e., Rosenzweig, K., Erdi, A. K., Macapinlac, H. A., Hu, Y. –C, Braban, L. E., Humm, J. L., Squire, O. D., Chui, C. –S., Larson, S. M., Yorke, E. D. Radiotherapy treatment planning for patients with non-small cell lung cancer using position emission tomography (PET) [J]. Radiotherapy and Oncology. 2002, 62(1):51-60.
[133] Mattes, D., Haynor, D. R., Vessells, H., Lewellen, T. K., Eubank, W. Nonrigid multimodality image registration. Proceedings of SPIE, Medical Imaging: Image Processing. 2001, 4322:1609-1620.
[134] Clarkson, M. J., Rueckert, D., King, A. P., Edwards, P. J., Hill, D. L. G., Hawkes, D. J. Registration of viedo images to tomographic images byu optimizing mutual information using texture mapping. Proceedings of Lecture Notes in Computer Science, Medical Image Computing and Computer-Assisted Intervention. 1999, 1679:579-588.
[135] Stephen, J., Mohan, M. T. Mutual information based registration of multimodal stereo videos for person tracking [J]. Computer Vision and Image Understanding. 2007, 106(2-3):270-287.
[136] Li, S. Markov random field modeling in computer vision. Spring-Verlag. 1995.
[137] Canny, J. A computational approach to edge detection [J]. IEEE Trans. on Pattern Analysis and Machine Intelligence. 1986, 8(6):679-698.
[138] Bergholm, F. Edge focusing [J]. IEEE Trans. Pattern Analysis and Machine Intelligence. 1987, 9(6):726-741.
[139] Deriche, R., Giraudon, G. A computational approach for corner and vertex detection [J]. International Journal of Computer Vision. 1993, 10(2):101-124.
[140] Lowe, D. G. Object recognition from local scale-invariant features. Proceedings of the International Conference on Computer Vision (ICCV). 1999, pp. 1150–1157.
[141] Lowe, D. G. Distinctive image features from scale-invariant keypoints [J]. International Journal of Computer Vision. 2004, 60(2):91-110.
[142] Ke, Y., Sukthankar, R. PCA-SIFT: A more distinctive representation for local image descriptors. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2004, pp.506-513.
[143] Press, W. H., Flannery, B. P., Teukolsky, S. A., Vettering, W. T. Numerical Recipes in C. Cambridge, U.K. : Cambridge Univ. Press, 1992.
[144] Collins, D. L., Zijdenbos, A. P., Kollokian, V., Sled, J. G., Kabani, N. L., Holmes, C. J., Evans, A. C. Design and construction of a realistic digital brain phantom [J]. IEEE Transactions on Medical Imaging. 1998, 17:463-468.
[145] Zhu, Y. -M., Cochoff, S. M. Likelihood Maximization Approach to Image registration [J]. IEEE Trans. on Image Processing, 2002, 11(12):1417-1426.
[146] Wang, M. Y., Maurer Jr., C. R., Fitzpatrick, J. M., Maciuman, R. J. An automatic technique for finding and localizing externally attached markers in CT and MR volume images of the head [J]. IEEE Transactions on Biomedical Engineering. 1996, 43:627-637.
[147] Sanchez, C. O., Thevenaz, P., Unser, M. Elastic registration of biological images using vector-spline regularization [J]. IEEE Transactions on Biomedical Engineering. 2005, 52(4):652-663.
[148] Walimbe, V., Dandekar, O., Mahmoud, F., Shekhar, R. Automated 3D elastic registration fro improving tumor localization in whole-body PET-CT from combine scanner. EMBS’06.28th Annual International Conference of the IEEE. 2006, pp.2799-2802.
[149] Christensen, G., Rabbitt, R. D. Deformable templates using large deformation kinematics [J]. IEEE Transactions on Image Process. 1996, 5:1435-1447.
[150] Christensen, G., Joshi, S., Miller, M. Volumetric transformation of brain anatomy [J]. IEEE Transactions on Medical Imaging. 1997, 16:864-877.
[151] Konard, J., Dubois, E. Bayesian estimation of motion vector fields [J]. IEEE Transaction on Pattern Analysis and Machine Intelligence. 1992, 14(9):910-927.
[152] Ashburner, J., Friston, K. J. Nornlinear spatial normalization using basis functions [J]. Human Brain Mapping. 1999, 7:254-266.
[153] Amit, Y. A nonlinear variational problem for image matching [J]. SIAM Journal on Scientific Computing. 1994, 15(1):207-224.
[154] Xie, Z., Farin, G. E. Image registration using hierarchical B-splines [J]. IEEE Transactions on Visusalization and Computer Graphics. 2004, 10(1):85-94.
[155] Guo, Y., Sivaramakrishna, R.., Lu, C., Suri, J. S., Laxminarayan, S. Breast image registration techniques: a survey [J]. Medical Biology Engineering Comput. 2006, 44:15-26.
[156] Heywang-Kobrunner, S. H., Beck, R. Contrast-Enhanced MRI of the Breast. Berlin, Germany: Springer-Verlag. 1995.
[157] Krishnan, S., Chenevert, T. L., Helvie, M. A., Londy, F. L. Linear Motion Correction in Three Dimensions Applied to Dynamic Gadolinium Enhanced Breast Imaging [J]. Medical Physics.1999,26: 707-714.
[158] Zuo, C. S., Jiang, A., Buff, B. L., Mahon, T. G., Wong, T. Z. Automatic Motion Correction for Breast MR Imaging [J]. Radiology. 1996, 198(3):903-906.
[159] Bookstein, F. L. Principal warps: thin-plate splines and the decomposition of deformations [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1989, 11(6):567-585.
[160] D, Shen, Resnick, S. M., D., Christos. 4D HAMMER image registration method for longitudinal study of brain changes [J]. Human Brain Mapping. 2003, June 18-22.
[2] 罗述谦. 医学图像配准技术. 国外医学生物医学工程分册. 1999, 22(1):1-7.
[3] Maintz, J. B. A., Max A. Viergever. A survey of medical image registration [J]. Medical Image analysis. 1998, 2(1):1-36.
[4] Hill, D. L. G., Batchelor, P. G.., Holden, M., Hawkes, D. J. Medical image registration [J]. Physics In Medicine and Biology. 2001, 46:1-45.
[5] Zitova, B., Flusser, J. Image registration methods: a survey [J]. Image and Vision Computing. 2003, 21:977-1000.
[6] Verbeeck, R. Rinciples and practive of a workstation for the pre-operative planning of stereotactic neurosurgical interventions: from implementation to clinical evaluation [Ph.D. thesis]. Belgium: Leuven, 1996.
[7] Gildenberg, P. L., Tasker, R. R. Textbook of stereotactic and functional neurosurgery. New York, NY: McGraw-Hill. 1998.
[8] Simon, D. A., O’Toole, R. V., Blackwell, M., Morgan, F., Digioia, A. M., Kanade, T. Accuracy validation in image-guided orthopaedic surgery [J]. In Medical Robotics and Computer Assisted Surgery. 1995, pp.185-192.
[9] Maurer, C. R., Fitzpartick, J. M., Galloway, R. L., Wang, M. Y., Maciunas, R. J., Allen, G. S. The accuracy of image-guided neurosurgery using implantable fiduical markers [J]. Computer Assosted Radiology. 1995, pp.1197-1202.
[10] Ellis, R. E., Toksvig-Larsen, S., Marcacci, M., Caramella, D., Fadda, M. A biocompatible fiduical marker for evaluating the accuracy of CT image registration [J]. Computer Assisted Radiology. 1996, 1124:693-698.
[11] Biswal, B. B., Hyde, J. S. Contour-based registration technique to differentitate between task-activated and head motion-induced calibration for freehand 3D ultrasound. Medical Image Computing and Computer-Assisted Intervention. 2000, 1935:462-471.
[12] Declerck, J., Feldmar, J., Goris, M. L., Betting, F. Automatic registration and alignment on a templated of cardiac stress and rest reoriented SPECT images [J]. IEEE Transactions on MedicalImaging. 1997, 16(6):727-737.
[13] Gilhuijs, K. G. A., Herk, M. van. Automatic on-line inspection of patient setup in radiation therapy using digital portal images [J]. Medical Physics. 1993, 20(3):667-677.
[14] Grimson, W. E. L., Ettinger, G. J., White, S. J., Lozano-Perez, T., Wells, W. M., Kikinis, R. An automation registration method for frameless stereotaxy, image guided surgery, and enhanced reality visualization [J]. IEEE Transactions on Medical Imaging. 1996, 15(2):129-140.
[15] Grzeszczuk, R., Tan, K. K.., Levin, D. N., Pelizzari, C. A., Hu, X., Chen, G. T. Y., Beck, R. N., Chen, C. T., Cooper, M., Milton, J., Spire, J. P., Towle, V. L., Dohrmann, G. J., Erickson, R. K. Retrospective fusion of radiographic and MR data for localization of subdural electrodes [J]. Journal of Computer Assisted Tomography. 1992, 16(5):7 64-773.
[16] Herring, J. L., Dawant, B. M., Maurer, C. R., Muratore, D. M., Galloway, R. L., Fitzpatrick, J. M. Surfact-based registration of CT images to physical space for image-guided surgery of the spine: a sensitivity study [J]. IEEE Transaction on Medical Imaging. 1998, 17(5):743-761.
[17] Szeliski, R., Lavallee, S. Matching 3-D anatomical surfaces with non-rigid deformations using octree-splines [J]. International Journal of Computer Vision. 1996, 18(2):171-186.
[18] Thirion, J. –P. Image matching as a diffusion process: an analogy with Maxwell’s demons [J]. Medical Image Analysis. 1998, 2(3):243-260.
[19] Bajcsy, R., Kovacic, S. Multiresolution elastic matching [J]. Computer Vision, Graphics and Image Processing. 1989, 46:1-21.
[20] Cachier, P., Pennec, X. 3D non-rigid registration by gradient descent on a Gaussian-windowed similarity measure using convolutions. In Matchematical Methods in Biomedical Image Analysis, IEEE Computer Society Press, Los Alamitos, CA. 2000, pp.182-189.
[21] Gee, J. C., Alsop, D. C., Aguirre, G. K. Effect of spatial normalization on alalysis of functional data. Proceeding of SPIE in Medical Imaging: Image Processsing. 1997, 3034:550-560.
[22] Lemieux, L., Wieshmann, U. C., Moran, N. F., Fish, D. R., Shorvon, S. D. The detection and significance of subtle change in mixed-signal brain lesions by serial MRI scan matching and spatial normalization [J]. Medical Image analysis. 1998, 2(3):227-242.
[23] Christensen, G. E., Joshi, S. C, Miller, M. I. Volumetric transformation of brain anatomy [J]. IEEE Transaction on Medical Imaging. 1997, 16(6):864-877.
[24] Gee, J. C., Haynor, D. R., Briquer, L. Le., Bajcsy, R. K. Advances in elastic matching theory and its implementation. Proceeding s of Computer Vision, Virtual Reality and Robotics in Medicine and Medical Robotics and Computer-Assisted Surgery. 1997, 1205:63-72.
[25] Ashburner, J., Friston, K. J. Nonlinear spatial normalization using basis functions [J]. Human Brain Mapping. 1999, 7:254-266.
[26] Woods, R. P., Cherry, S. R., Mazziotta, J. C. Rapid automated algorithm for aligning and reslicing PET images [J]. Journal of Computer Assisted Tomography. 1992, 16:620-633.
[27] Woods, R. P., Mazziotta, J. C., Cherry, S. R. MRI-PET registration with automated algorithm [J]. Journal of Computer Assisted Tomography. 1993, 17(4):536-546.
[28] Hill, D. L. G., Hawkes, D. J., Harrison, N. A., Ruff, C. F. A strategy for automated multimodality image registration incorporating anatomical knowledge and imager characteristics. Information Processing in Medical Imaging. 1993, 687:182-196.
[29] Collignon, A. Multi-modality medical image registration by maximization of mutual information [Ph.D. thesis]. Leuven: Catholic University of Leuven. 1998.
[30] Collignon, A., Maes, F., Delaere, D., Vandermeulen, D, Suetens, P., Marchal, G. Automated multi-modality image registration based on information theory. Information Processing in Medical Imaging. 1995, pp.263-274.
[31] Viola, P. A., Wells III, W. M. Alignment by maximization of mutual information. International Conference on Computer Vision, IEEE Computer Society Press. 1995, pp.16-23.
[32] Viola, P. A. Alignment by maximization of mutual information [Ph.D. thesis]. Boston: Massachusetts Institute of Technology. 1995.
[33] Wells III, W. M., Viola, R., Kikinis, R. Multi-modal volume registration by maximization of mutual information. Medical Robotics and Computer Assisted Surgery. 1995, pp.55-62.
[34] Studholme, C., Hill, D.L.G., Hawkes, D.J. Multiresolution voxel similarity measures for MR-PET registration. Proceedings of the International Conference on Information Processing in Medical Imaging (IPMI). 1995, 3:287-298.
[35] Studholme, C., Hill, D.L.G., Hawkes, D.J. Automated 3-D registration of MR and CT images of the head [J]. Medical Image Analysis. 1996, 1(2):163-175.
[36] Studholme, C., Hill, D.L.G., Hawkes, D.J. Automated three-dimensional registration of magnetic resonance and positron emission tomography brain images by multiresolution optimization of voxel similarity measure. Medical Physics. 1996, 24(1):25-35.
[37] Wells III, W. M., Viola, Atsumi, H., Nakajima, S., Kikinis, R. Multi-modal volume registration by maximization of mutual information. Medical Image Analysis. 1996, 1:35-51.
[38] Meyer, C. R., Boes, L. J., Kim, B., Bland, P. H., Zasadny, K. R., Kison, P. V., Koral, K., Frey, K. A., Wahl, R. L. Demonstration of accuracy and clinical versatility of mutual information for automaticmultimodality image fusion using affine and thin-plate spline warped geometric deformations [J]. Medical Image Analysis. 1996, 1(3):195-206.
[39] Maes, F., Vandermeulen, D., Suetens, P. Medical image registration using mutual information [J]. Proceedings of the IEEE. 2003, 91:1699-1722.
[40] Belis, M., Guiasu, S. A quantitative-qualitative measure of information in cybernetic systems [J]. IEEE Trans. Inform. Theory. 1968, 14:593-594.
[41] Taneja, H. C., Tuteja, R. K. Characterization of a quantitative-qualitative measure of relative information [J]. Information Sciences. 1984, 33:217-222.
[42] Taneja, H. C. On the quantitative-qualitative measure of relative information [J]. Information Sciences. 1984, 33:223-227.
[43] Kadir, T., Brady, M. Saliency, scale and image descriptions [J]. International Journal of Computer Vision. 2001, 45:83-105.
[44] Kadir, T. Scale, saliency and Scene description [Ph.D. thesis]. Oxford: University of Oxford. 2002.
[45] Huang, X., Sun, Y., Metaxas, D., Sauer, F., Xu, C. Hybrid image registration based on configural matching of scale-invariant salient region features. Proceedings of the Conference on Computer Vision and Pattern Recognition Workshop. 2004, pp.167.
[46] Lao, Z., Shen, D., Jawad, A., Karacali, B., Liu, D., Melhem, E., Bryan, N., Davatzikos, C. Automated segmentation of white matter lesions in 3D brain MR images using multivariate pattern classification. Proceedings of the 3rd IEEE International Symposium on Biomedical Imaging (ISBI). 2006, pp.307-310.
[47] Hata, N., Dohi, T., Warfield, S. K., Wells, W. M., Kikinis, R., Jolesz, F.A. Multimodality deformable registration of pre- and intraoperative images for MRI-guided brain surgery. Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). 1998, pp 1067-1074.
[48] Bharatha, A., Hirose, M., Hata, N., Warfield, S. K., Ferrant, M., Zou, K. H., Suarez-Santana, E., Ruiz-Alzola, J., D'Amico, A., Cormack, R. A., Kikinis, R., Jolesz, F.A., Tempany, C. M. C. Evaluation of three-dimensional finite element-based deformable registration of pre- and intraoperative prostate imaging. Medical Physics. 2001, 28(12): 2551-2560.
[49] Toga, A. W., Thompson, P. The role of image registration in brain mapping [J]. Image and Vision Computing. 2001, 19(1-2): 3-24.
[50] Pluim, J. P. W., Maintz, J. B., Viergever, M. A. Mutual-information-based registration of medical images: a survey [J]. IEEE Transaction Medical Imaging. 2003, 22:986-1004.
[51] Pettey, D. J., Gee, J. C. Using a linear diagnostic function and non-rigid registration to search for morphological differences between populations: an example involving the male and female corpus callosum. Proceedings of the International Conference on Information Processing in Medical Imaging (IPMI). 2001, pp. 372-379.
[52] Pompe, B., Blidh, P., Hoyer, D., Eiselt, M. Using mutual information to measure coupling in the cardiorespiratory system [J]. IEEE Engineering in Medicine and Biology Magazine. 1998, 17(6): 32-39.
[53] Holden, M., Hill, D. L. G., Jarosz, J. M., Cox, T. C. S., Rohlfing, T., Goodey, J., Hawkes, D. J. Voxel similarity measures for 3-D serial MR brain image registration [J]. IEEE Transaction on Medical Imaging. 2000, 19:94-102.
[54] Freire, L., Roche, A., Mangin, J. –F. What is the best similarity measure for motion correction in fMRI a time series [J]? IEEE Transaction on Medical Imaging. 2002, 21:470-484.
[55] Radau, P. E., Slomka, P. J., Julin, P., Svensson, L., Wahlund, L. –o. Automated segmentation and registration technique for hmpao-spect imaging of Alzheimer’s patients. Proceedings of SPIE on Medical Imaging: Image Processing. 2000, 3979:372-384.
[56] Fei, B., Wheaton, A., Lee, Z., Duerk, J. L., Wilson, D. L. Automatic MR volume registration and its evaluation for the pelvis and prostate [J]. Physics in Medicnine and Biology. 2002, 47(5):823-838.
[57] Slomka, P. J., Mandel, J., Downey, D., Fenster, A. Evaluation of voxel-based registration of 3-D power Doppler ultrasound and 3-D magnetic resonance angiographic images of carotid arteries [J]. Ultrasound in Medicine and Biology. 2001, 27(7):945-955.
[58] Carrillo, A., Duerk, J. L., Lewin, J. S., Wilson, D. L. Semi-automic 3-D image registration as applied to interventional MRI liver cancer treatment [J]. IEEE Transactions on Medical Imaging. 2000, 19(3):175-185.
[59] Holden, M., Denton, E. R. E., Jarosz, J. M., Cox, T. C. S., Studholme, C., Hawkes, D. J., Hill, D. L. G. Detecting small anatomical changes with 3D serial MR subtraction images. Proceedings of SPIE in Medical imaging: Image Processing. 1999, 3661:44-55.
[60] Holden, M., Hill, D. L. G., Denton, E. R. E., Jarosz, J. M., Cox, T. C. S., Rohlfing, T., Goodey, J., Hawkes, D. J. Voxel similarity measures for 3-D serial MR brain iamge registration [J]. IEEE Transactions on Medical Imaging. 2000, 19(2):94-102.
[61] Studholme, C., Cardenas, V., Weiner, M. Multi scale image and multi scale deformation of brain anatomy for building average brain atlases. Proceedings of SPIE in Medical Imaging: Image Processing. 2001, 4322:557-568.
[62] Studholme, C., Novotny, E, Zubal, I. G., Duncan, J. S. Estimating tissue deformation between functional images induced by intracranial electrode implantation using anatomical MRI [J]. NeuroImage. 2001, 13(4):561-576.
[63] Shekhar, K., Zagrodsky, V. Mutual information-based rigid and nonrigid registration of ultrasound volumes [J]. IEEE Transactions on Medical Imaging. 2002, 21(1):1660-1668.
[64] Meyer, C. R., Boes, J. L., Kim, B., Bland, P. H., Lecarpentier, G. L., Fowlkes, J. B., Roubidoux, M. A., Carson, P. L. Semiautomatic registration of volumetric ultrasound scans [J]. Ultrasound in Medicine and Biology. 1999, 25(3):339-347.
[65] Zagrodsky, V., Shekhar, R., Cornhill, J. F. Mutual information based registration of cardiac ultrasound volumes. Proceedings of SPIE in Medical Imaging: Image Processing. 2000, 3979:1605-1614.
[66] Ding, L., Goshtasby, A., Satter, M. Volume image registration by template matching [J]. Image and Vision Computing. 2001, 19(12):821-832.
[67] Holden, M., Schnabel, J. A., Hill, D. L. G. Quantifying small changes in brain ventricular volume using non-rigid registration. Proceedings of Computer Science in Medical Image Computing and Commputer-Assisted Intervention, Lecture Notes. 2001, 2208:49-56.
[68] Studholme, C. Measures of 3D medical image aligment [Ph.D. thesis]. London: University of Londen. 1997.
[69] Zollei, L., Grimson, E., Norbash, A., Wells, W. 2D-3D rigid registration of X-ray fluoroscopy and CT images using mutual information and sparsely samples histogram estimators. Proceedings of IEEE Computer Society in Computer Vision and Pattern Recognition. 2001, 2:696-703.
[70] Hayton, P. M., Brady, M., Smith, S. M., Moore, N. A non-rigid registration algorithm for dynamic breast MR images [J]. Artifical Intelligence. 1999, 114(1-2):125-156.
[71] Plattard, D., Soret, M., Troccaz, J., Vassal, P, Giraud, J. –Y., Champleboux, G.., Artignan, X., Bolla, M. Patient setup using portal images: 2D/2D image resgistration using mutual information [J]. Computer Aided Surgery. 2000, 5(4):246-262.
[72] Studholme, C., Hill, D. L. G., Hawkes, D. J. A normalized entropy measure for multi-modality image alignment. Proceedings of SPIE, Medical Imaging: Image Processing. 1998, 3338: 132-143.
[73] Studholme, C., Hill, D. L. G., Hawkes, D. J. An overlap invarivant entropy measure of 3-D medical image alignment [J]. Pattern Recognition. 1999, 32(1):71-86.
[74] Hill, D. L. G., Maurer, C. R., Maciunas, Jr., R. J., Barwise, J. A., Fitzpatrick, J. M., Wang, M. Y. Measurement of intraoperative brain surface deformation under a craniotomy [J]. Neurosurgery. 1998, 43:514-526.
[75] Likar, B., Pernus, F. A hierarchical approach to elastic registration based on mutual information. Image and Vision Computing. 2001, 19:33-44.
[76] Studholme, C., Constable, R. T., Duncan, J. S. Accurate alignment of functional EPI data to anatomical MRI using a physics-based distortion model [J]. IEEE Transactions on Medical Imaging. 2000, 19:1115-1127.
[77] Rueckert, D., Clarkson, M. J., Hill, D. L. G.., Hawkes, D. J. Non-rigid registration using higher-order mutual information. Proceedings of SPIE, Medical Imaging: Image processing. 2000, 3979:438-447.
[78] Studholme, C., Hill, D. L. G.., Hawkes, D. J. Incorporating connected region labeling into automated image registration using mutual information. Proceedings of the IEEE workshop on Mathematical Methods in Biomedical Image Analysis. 1996, pp. 23-31.
[79] Pluim, J. P. W., Maintz, J. B., Viergever, M. A. Image registration by maximization of combined mutual information and gradient information. IEEE Trans. Medical. Imaging, 2000, 19:809-814.
[80] Rodríguez-Carranza, C. E., Loew, M. H. A weighted and deterministic entropy measure for image registration using mutual information. Proceedings of SPIE, Medical Imaging: Image Processing. 1998, 3338:155-166.
[81] Rodríguez-Carranza, C. E., Loew, M. H. Global optimization of weighted mutual information for multimodality image registration. Proceedings of SPIE, Medical Imaging: Image Processing. 1999, 3338:89-96.
[82] Rui Gan, Albert C. S. Chung. Distance-Intensity for Image Registration. CVBIA. 2005, pp. 281-290.
[83] Rui Gan, Albert C. S. Chung. Multi-dimensional Mutual Information Based Robust Image Registration Using Maximum Distance-Gradient-Magnitude. IPMI. 2005, pp.210-221.
[84] Holden, M., Griffin, L. D., Saeed, N., Hill, D. L. G. Multi-channel mutual information using scale space. Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). 2004, pp.797-804.
[85] Shen, D., Davatzikos, C. HAMMER: Hierarchical attribute matching mechanism for elastic registration [J]. IEEE Trans. on Medical Imaging. 2002, 21(11): 1421-1439.
[86] Hongxia Luan, Feihu Qi, Zhong Xue, Liya Chen, Dinggang Shen. Multimodality image registration by maximization of quantitative-qualitative measure of mutual information [J]. Pattern Recognition. 2007.
[87] Hongxia Luan, Feihu Qi, Dinggang Shen. Multi-model image registration by quantitative-qualitative measure of mutual information (Q-MI). Computer Vision for Biomedical Image applications, ICCV 2005 workshop, October 21, Beijing, China. 2005, pp: 378-387.
[88] Krol, Z., Zerfass, P., Rymon-Lipinski, B., Jansen, T., Hauck, W., Zeilhofer, H. –F., Sader, R., Keeve, E. computer aided osteotomy design for harvesting autologous bone grafts in reconstructive surgery. Proceedings of SPIE, Medical Imaging: Visualization, Image Display, and Image-guided Procedures. 2001, 4319:244-251.
[89] Brinkmann, B. H., O’Brien, D. P., S. Aharon, O’Connor, M. K., Mullan, B. P., Hanson, D. P., Robb, R. A. Quantitative and clinical analysis of SPECT image registration fro epilepsy studies [J]. Journal of Nuclear Medicine. 1999, 40(7):1098-1105.
[90] Koral, K. F., Lin, S., Fessler, J. A., Kaminski, M. S., Wahl, R. L. Prelininary results from intensity-based CT-SPECT fusion in I-131 anti-B1 monoclonal-antibody therapy of lymphoma [J]. Cancer. 1997, 80(12):2538-2544.
[91] Sanjay-gopal, S., Chan, H. P., Wilson, T., Helvie, M., Petrick, N., Saniner, B. A regional registration technique for automated interval change analysis of breast lesions on mammograms [J]. Medical Physics. 1999, 26(12):2669-2679.
[92] Denton, E. R. E., Holden, M., Christ, E., Jarosz, J. M., RussellHones, D., Goodey, J., Cox, T. C. S., Hill, D. L. G. The identification of cerebral volume changes in treated growth hormonedeficient adults using serial 3D MR image processing [J]. Journal of Computer Assisted Tomography. 2000, 24(1):139-145.
[93] Ourselin, S., Roche, A., Prima, S., Ayache, N. Block matching: a general framework to improve robustness of rigid registration of medical images. Proceedings of Lecture Notes in Computer Science, Medical Image Computing and Computer-Assisted Intervention. 2000, 1935:557-566.
[94] Van Laere, K., Koole, M., D’Asseler, Y., Versijpt, J., Audenaert, K., Dumont, F., Dierckx, R. Automated stereotactic standardization of brain SPECT receptor data using single-photon transmission images [J]. Journal of Nuclear Medicine. 2001, 42(2):361-375.
[95] Ritter, N., Owens, R., Cooper, J., Eiklboom, R. H., van Saarloos. P. P. Registration of stereo andtemporal images of the retina [J]. IEEE Transactions on Medical Imaging. 1999, 18(5):404-418.
[96] Jenkinson, M., Smith, S. A global optimization method for robust affine registration of brain images [J]. Medical Image analysis. 2001, 5(2):143-156.
[97] Hill, D. L. G., Maurer, C. R., Jr., Studholme, C., Fitzpatrick, J. M., Hawkes, D. J. Correcting scaling errors in tomographic images using a nine degree of freedom registration algorithm [J]. Journal of Computer Assisted Tomography. 1998, 22(2):317-323.
[98] Holden, M. Registration of 3D serial MR brain images [Ph.D. thesis]. London: University of London. 2001.
[99] Meyer, c., Boes, J., Kim, B., Bland, P. Evaluation of control point selection in automatic, mutual information driven, 3D warping. Proceedings of Lectures Notes in Computer Science. 1998, 1496:944-951.
[100] Horsfield, M. A. Mapping eddy current induced fields for the correction of diffusion-weighted echo planar images [J]. Magnetic Resonance Imaging. 1999, 17(9):1335-1345.
[101] Rueckert, D., Sonoda, L. I., Hayes, C., Hill, D., Leach, M. O., Hawkes, D. J. Nonrigid registration using free-form deformations: application to breast MR images [J]. IEEE Transactions on Medical Imaging. 1999, 18(8):712-721.
[102] Otte, M. Elastic registration of fMRI data using Bezier-spline transformations [J]. IEEE Transactions on Medical Imaging. 2001, 20(3):193-206.
[103] Likar, B., Pernus, F. Registration of serial transverse sections of muscle fibres [J]. Cytometry. 1999, 37(2):93-106.
[104] Hellier, P., Barillot, C. Multimodal non-rigid warping for correction of distortions in function MRI. Proceedings of Lecture Notes in Computer Science, Medical Image Computing and Computer-Assisted Intervention. 2000, 1935:512-520.
[105] Likar, B., Pernus, F. A Hierarchical approach to elastic registration based on mutual information [J]. Image and Vision Computing. 2001, 19(1-2):33-44.
[106] Maintz, J. B. A., Meijering, E. H. W., Viergever, M. A. General multimodal elastic registration based on mutual information. Proceedings of SPIE, Medical Imaging; Image Processing. 1998, 3338:144-154.
[107] Zhu, Y. –M., Cochoff, S. M. Influence of implementation parameters on registration of MR and SPECT brain images by maximization of mutual information [J]. Journal of Nuclear Medicine. 2002, 43(2):160-166.
[108] Thevenaz, P., Unser, M. Optimization of mutual information for multiresolution imageregistration [J]. IEEE Transactions on Image Processing. 2000, 9(12):2083-2099.
[109] Netsch, T., Rosch, P., Weese, J., van Muiswinkel, A., Desmedt, P. Grey value-based 3-D registration of functional MRI time-series: comparison of interplolation order and similarity measure. Proceedings of SPIE, Medical Imaging: Image Processing. 2000, 3979:1148-1159.
[110] F. Maes. Segmentation and registration of multimodal medical Images: from theory, implementation and validation to a useful tool in clinical practice [Ph.D. thesis]. Leuven: Catholic University of Leuven. 1998.
[111] Pluim, J. P. W., Maintz, J. B., Viergever, M. A. Interplation artifacts in mutual information-based image registration [J]. Computer Vision and Image Understanding. 2000, 77:211-232.
[112] Tsao, J. Interpolation artifacts in multimodality image registration based on maximization of mutual information [J]. IEEE Transaction on Medical Imaging. 2003, 22(7):854-965.
[113] Kim, J., Fisher, J. W., Tsai, A., Wible, C., Willsky, A. S., Wells. W. M. Incorporating spatial priors into an information theoretic approach for fMRI data analysis. Proceedings of Lecture Notes in Computer Science, Medical Image Computing and Computer-Assisted Intervention. 2000, 1935:62-71.
[114] Jenkinson, M., Bannister, P., Brady, m., Smith, S. Improved optimization for the robust and accurate linear registration and motion correction of brain images [J]. NeuroImage. 2002, 17:825-841.
[115] Maes, F., Vendermeulen, D., Sutens, 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.
[116] Boes, J. L., Meyer, C. R. Multi-variate mutual information for registration. Proceedings of Lecture Notes in Computer Science, Medical Image Computing and Computer-Assisted Intervention. 1999, 1679:606-612.
[117] Thevenaz. P., Unser, M. A pyramid approach to sub-pixel image fusion based on mutual information. International Conference on Image Processing. 1996, 265-268.
[118] Bansal, R., Staib, L., Chen, Z., Rangarajan, A., Kinsely, J., Nath, R., Duncan, J. A minimax entropy registration framework for patient setup verification in radiotherapy [J]. Computer Aided Surgery. 1999, 4(6):287-304.
[119] Kim, j., Fessler, J. A., Lam, K. L., Balter, J. M., Ten Haken, R. K. A feasibility study on mutual information based set-up estimator for radiotherapy [J]. Medical Physics. 2001,28(12):2507-2517.
[120] Rupin, D., Rafeef, A., Mark, P., Jennie, S., Paul, S. Registration of 2D to 3D joint images using phase-based mutual information. Proceedings of SPIE, Medical Imaging: Image Processing. 2007, 6512:1-9.
[121] Chen, X., Brady, M., Lok-Chuen Lo, J., Moore, N. Simultansous Segmentaion and Registration of Contrast-Enhanced Breast MRI. IPMI. 2005, pp.126-137.
[122] Andresson, J. L. R., Thurfjell, L. A multivariate approach to registration of dissimilar tomographic images [J]. European journal of Nuclear Medicine. 1999, 26(7):718-733.
[123] Cover, T. M., Thomas, J. A. Elements of Information Theory. New York. 1975.
[124] Mastuda, H. Physical nature of higher-order mutual information: intrinsic correlations and frustration [J]. Physical Review E. 2000, 62(3):3096-3102.
[125] Lynch, J. A., Peterfy, C. G., White, D. L., Hawkins, R. A., Genant, H. K. MRI-SPECT image registration using multiple MR pulse sequences to examine osteoarthritis of the knee. Proceedings of SPIE, Medical Imaging: Image Processing. 1999, 3661:68-77
[126] Krucker, J. F., Meyer, C. R., LeCarpentier, g. L., Fowlkes, J. B., Carson, P. L. 3D partial compounding of ultrasound images using image-based nonrigid registration [J] .Ultrasound in Medicine and Biology. 2000, 26(9):1475-1488.
[127] West, J., Fitzpatrick, J. M., Wang, M. Y., Dawant, B. M., Maurer Jr, C. R., Kessler, R. M., Maciunas, R. J., Barillot, C., Lemoine, D., Collignon, A., Maes, F., Suetens, P., Vandermeulen, D., van den Elsen, P. A., Napel, S., Sumanaweera, T. S., Harkness, B., Hemler, P. F., Hill, D. L. G., Hawkes, D. J., Studholme, C., Maintz, J. B., Viergever, M. A., Malandain, G., Woods, R. P. Comparison and evaluation of retrospective intermodality brain image registration techniques [J]. Journal Comput Assist Tomogr. 1997, 21(4):554-566.
[128] Nikou, C., Heitz, F., Armspach, J. –P. Robust voxel similarity metrics for the registration of dissimilar single and multimodal images [J]. Pattern Recognition. 1999, 32(8):1351-1368.
[129] Holmes, D. R., Camp, J., Robb, R. A. Evaluation of search strategies and cost functions in optimizing voxel-based image registration. Proceedings of SPIE, Medical Imaging: Image Display. 1996, 2707:554-562.
[130] Roche, A., Pennec, X., Malandain, G., Ayache, N. Rigid registration of 3-D ultrasound with MR images: a new approach combining intensity and gradient information [J]. IEEE Transactions on Medical Imaging. 2001, 20(10):1038-1049.
[131] Blackall, J. M., Rueckert, D., Maurer, C. R., Jr., Penney, G. P., Hill, D. L. G., Hawkes, D. J. Animage registration approach to automated calibration for freehand 3D ultrasound. Proceedings of Lecture Notes in Computer Science, Medical Image Computing and Computer-Assisted Intervention. 2000, 1935:462-471.
[132] Erdi, Y. e., Rosenzweig, K., Erdi, A. K., Macapinlac, H. A., Hu, Y. –C, Braban, L. E., Humm, J. L., Squire, O. D., Chui, C. –S., Larson, S. M., Yorke, E. D. Radiotherapy treatment planning for patients with non-small cell lung cancer using position emission tomography (PET) [J]. Radiotherapy and Oncology. 2002, 62(1):51-60.
[133] Mattes, D., Haynor, D. R., Vessells, H., Lewellen, T. K., Eubank, W. Nonrigid multimodality image registration. Proceedings of SPIE, Medical Imaging: Image Processing. 2001, 4322:1609-1620.
[134] Clarkson, M. J., Rueckert, D., King, A. P., Edwards, P. J., Hill, D. L. G., Hawkes, D. J. Registration of viedo images to tomographic images byu optimizing mutual information using texture mapping. Proceedings of Lecture Notes in Computer Science, Medical Image Computing and Computer-Assisted Intervention. 1999, 1679:579-588.
[135] Stephen, J., Mohan, M. T. Mutual information based registration of multimodal stereo videos for person tracking [J]. Computer Vision and Image Understanding. 2007, 106(2-3):270-287.
[136] Li, S. Markov random field modeling in computer vision. Spring-Verlag. 1995.
[137] Canny, J. A computational approach to edge detection [J]. IEEE Trans. on Pattern Analysis and Machine Intelligence. 1986, 8(6):679-698.
[138] Bergholm, F. Edge focusing [J]. IEEE Trans. Pattern Analysis and Machine Intelligence. 1987, 9(6):726-741.
[139] Deriche, R., Giraudon, G. A computational approach for corner and vertex detection [J]. International Journal of Computer Vision. 1993, 10(2):101-124.
[140] Lowe, D. G. Object recognition from local scale-invariant features. Proceedings of the International Conference on Computer Vision (ICCV). 1999, pp. 1150–1157.
[141] Lowe, D. G. Distinctive image features from scale-invariant keypoints [J]. International Journal of Computer Vision. 2004, 60(2):91-110.
[142] Ke, Y., Sukthankar, R. PCA-SIFT: A more distinctive representation for local image descriptors. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2004, pp.506-513.
[143] Press, W. H., Flannery, B. P., Teukolsky, S. A., Vettering, W. T. Numerical Recipes in C. Cambridge, U.K. : Cambridge Univ. Press, 1992.
[144] Collins, D. L., Zijdenbos, A. P., Kollokian, V., Sled, J. G., Kabani, N. L., Holmes, C. J., Evans, A. C. Design and construction of a realistic digital brain phantom [J]. IEEE Transactions on Medical Imaging. 1998, 17:463-468.
[145] Zhu, Y. -M., Cochoff, S. M. Likelihood Maximization Approach to Image registration [J]. IEEE Trans. on Image Processing, 2002, 11(12):1417-1426.
[146] Wang, M. Y., Maurer Jr., C. R., Fitzpatrick, J. M., Maciuman, R. J. An automatic technique for finding and localizing externally attached markers in CT and MR volume images of the head [J]. IEEE Transactions on Biomedical Engineering. 1996, 43:627-637.
[147] Sanchez, C. O., Thevenaz, P., Unser, M. Elastic registration of biological images using vector-spline regularization [J]. IEEE Transactions on Biomedical Engineering. 2005, 52(4):652-663.
[148] Walimbe, V., Dandekar, O., Mahmoud, F., Shekhar, R. Automated 3D elastic registration fro improving tumor localization in whole-body PET-CT from combine scanner. EMBS’06.28th Annual International Conference of the IEEE. 2006, pp.2799-2802.
[149] Christensen, G., Rabbitt, R. D. Deformable templates using large deformation kinematics [J]. IEEE Transactions on Image Process. 1996, 5:1435-1447.
[150] Christensen, G., Joshi, S., Miller, M. Volumetric transformation of brain anatomy [J]. IEEE Transactions on Medical Imaging. 1997, 16:864-877.
[151] Konard, J., Dubois, E. Bayesian estimation of motion vector fields [J]. IEEE Transaction on Pattern Analysis and Machine Intelligence. 1992, 14(9):910-927.
[152] Ashburner, J., Friston, K. J. Nornlinear spatial normalization using basis functions [J]. Human Brain Mapping. 1999, 7:254-266.
[153] Amit, Y. A nonlinear variational problem for image matching [J]. SIAM Journal on Scientific Computing. 1994, 15(1):207-224.
[154] Xie, Z., Farin, G. E. Image registration using hierarchical B-splines [J]. IEEE Transactions on Visusalization and Computer Graphics. 2004, 10(1):85-94.
[155] Guo, Y., Sivaramakrishna, R.., Lu, C., Suri, J. S., Laxminarayan, S. Breast image registration techniques: a survey [J]. Medical Biology Engineering Comput. 2006, 44:15-26.
[156] Heywang-Kobrunner, S. H., Beck, R. Contrast-Enhanced MRI of the Breast. Berlin, Germany: Springer-Verlag. 1995.
[157] Krishnan, S., Chenevert, T. L., Helvie, M. A., Londy, F. L. Linear Motion Correction in Three Dimensions Applied to Dynamic Gadolinium Enhanced Breast Imaging [J]. Medical Physics.1999,26: 707-714.
[158] Zuo, C. S., Jiang, A., Buff, B. L., Mahon, T. G., Wong, T. Z. Automatic Motion Correction for Breast MR Imaging [J]. Radiology. 1996, 198(3):903-906.
[159] Bookstein, F. L. Principal warps: thin-plate splines and the decomposition of deformations [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1989, 11(6):567-585.
[160] D, Shen, Resnick, S. M., D., Christos. 4D HAMMER image registration method for longitudinal study of brain changes [J]. Human Brain Mapping. 2003, June 18-22.