基于非刚性配准的图像分割研究及其在脑部MRI图像中的应用
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摘要
图像分割在医学图像分析中起着至关重要的作用。医学图像分割的目的是将一幅图像分成不重叠的多个区域,各区域对应人体明确的解剖结构。采用医学MRI图像,将人脑的不同器官分割出来并分析是临床诊断和辨识脑部疾病进程的重要手段。医学图像的复杂性使医学图像分割是一个非常复杂的任务,借助医学图谱提供的先验知识有助于更好的完成分割任务。配准技术是将医学先验知识融入分割过程的一个重要途径,形成了一种新的分割技术,即基于配准的分割。该类方法采用配准得到模板图像变形为目标图像的空间变换,然后利用这个空间变换将与模板图像相对应的已知分割结果,即图谱信息,映射到目标图像,从而得到对目标图像感兴趣区域的分割。所以,基于配准的分割质量主要取决于配准质量。
人体结构的复杂性使得简单的刚性配准无法表示人体复杂结构的局部细节,而这些细节却是临床诊断的重要依据,因此有必要采用具有更高自由度的非刚性配准技术。同刚性配准相比,非刚性配准还不成熟,如何建立合理的形变模型,如何提高非刚性配准的速度、精度以及对非刚性配准的评估都需要进一步的研究。
在目前的非刚性配准技术中,基于物理模型的非刚性配准以其特有的描述组织自然行为的能力,成为该领域中的一个重要分支。本文在深入研究基于热力学扩散模型的Demons非刚性配准技术的基础上,就灰度与空间标准化、变形场的正则化、先验知识对配准算法的性能影响等问题展开了研究,并提出了具有拓扑保持性的适用于单模态图像配准的混合灰度与特征信息的非刚性配准算法,然后将其应用到3D脑深层灰质结构分割中。具体成果介绍如下：
1.分析、优化并实现了基于扩散模型的]Demons非刚性配准算法。
基于物理模型的配准是一类重要的非刚性配准技术,其中Demons非刚性配准是该类方法中适用于单模态图像配准的一个优秀代表。本文对Demons算法及其重要的改进算法-主动Demons算法进行了深入细致的分析和算法实现,重点研究了该算法的两个重要参数,弹性系数和均化系数,及其形变驱动力的局限性,得出了实际应用时参数设置的一般性原则,并给出了改进的形变驱动力。
2.提出了同时完成灰度与空间标准化的非刚性配准算法。
以两图像灰度差的平方和作为衡量图像相似程度的非刚性配准算法完全依赖图像的灰度信息,其假设前提是两图像中对应像素点的灰度相同。然而,对于MRI图像,受不同扫描仪器、不同参数设置以及不均匀场、干扰、噪声等影响,难以满足这样的假设条件,需要进行灰度匹配预处理。另外,对于不同个体间的配准问题,不同个体的生理差异较大。过大的图像空间位置差异也会影响非刚性配准技术的配准质量,因此需要预先进行空间标准化处理。本文研究了非刚性配准的灰度和空间标准化问题,结合Demons算法,提出了一种分步配准策略,不仅能够完成灰度和空间的同步标准化,而且具有较高的描述局部微小形变的能力。
3.提出了具有拓扑保持性的Demons非刚性配准算法。
Demons算法的一个主要缺陷是不能从理论上保证配准得到的空间变换具有拓扑保持性。尽管该算法试图通过双射和变形场平滑技术得到具有拓扑不变性的空间变换,然而其可靠性未能得到理论证明和实验支持。该算法得到的是一个稠密的变形场,图像具有很高的变形自由度。高自由度意味着有更多的像素可以自由运动,如果没有附加约束,不能保证得到的空间变换是合理的,可以实现的。因此本文重点研究了变形场的约束问题,通过分析矢量场关键点特性,提出了在尽量保持原有变形场几何特性的前提下校正原变形场拓扑性的非刚性配准算法,并通过实验对算法性能进行了评价。
4.提出了基于灰度与形状混合信息的非刚性配准算法。
人脑的深层灰质结构,如尾状核、壳体、苍白球、丘脑、海马体等等,与帕金森氏综合征、癫痫、痴呆、克雅氏病等脑部疾病关系密切,通过分析这些结构的体积、形状变化等有助于相关的疾病诊断。然而这些结构形状复杂,体积小,在核磁共振图像中,它们的边界模糊,受部分容积效应的影响大。因此,深层脑结构的全自动分割是一个具有挑战性的问题。在已有的研究中,基于配准技术的分割是解决该问题一种有效手段。本文通过分析基于灰度的非刚性配准算法的基本原理和不足,在配准能量函数中引入形状相似度项,提出了新的灰度信息与形状信息相结合的非刚性配准算法。算法适用于相似灰度多目标分割问题,在分割体积小、形状复杂、边缘模糊的深层脑结构的应用中得到了较好的结果。
Image segmentation plays an important role in medical image analysis. Medical image segmentation aims to divide an entire image into different non-overlapping regions. These regions represent different anatomical structures clearly. Segmenting and analyzing anatomical structures from brain MRI images is an effective method to diagnose brain diseases and track their evolvement. However the complexities of medical images make medical image segmentation a challenging task. Fortunately, the a priori knowledge provided by the atlas can contribute much to a better segmentation. Image registration is a good technique to fuse medical priori knowledge into segmentation procedure, which forms a new image segmentation technique, registration based segmentation. In registration based segmentation, an optimal spatial transformation deforming the source image into the target image is first obtained. Then the labeled structures in the atlas (correspondence to the source image) are mapped to the target image based on the obtained optimal spatial transformation. Finally, the required segmentation will be acquired easily. Obviously, the segmentation quality mainly depends on the registration quality.
Using simple rigid registration to describe subtle local differences between individuals is insufficient due to the complexity of the human body structures. Therefore non-rigid registration techniques should be used to solve the problem. Non-rigid registration approaches usually have higher degree of freedom, and can describe more complex nonlinear deformations. Compared to the rigid registration, non-rigid registration is not mature. There are many problems to be solved, such as establishing a reasonable deformation model, improving the registration speed and accuracy, developing convincing algorithm evaluation criteria, and so on.
Non-rigid registration based on physical models is an important branch in the current techniques, because they have unique abilities to describe the physical behaviors of organism. In this dissertation, we studied diffusion model based Demons non-rigid registration algorithm in depth, which originated from thermodynamic theory. The main researches focus on the intensity and spatial normalization, regularization method of the deformation field, the contributions of a priori knowledge to non-rigid registration and image segmentation. In segmenting deep brain internal structures applications, a hybrid intensity and shape knowledge non-rigid registration algorithm is proposed. The major contributions of this dissertation are as follows:
1. Analyze, optimize, and implement diffusion model based Demons non-rigid registration algorithm.
Demons non-rigid registration is one of excellent algorithms in physical model based registration. It is suitable to register two images within the same modality. In this dissertation, the Demons algorithm and its variant, active Demons algorithm, are studied in depth. Two important parameters, elasticity parameter and equalization parameter, and the limitations of the deformation force are analyzed carefully. Then instructive parameter setting principles and an improved deformation force are proposed.
2. Propose a more localized non-rigid registration algorithm, which can complete spatial and intensity normalization simultaneously.
Non-rigid registration algorithm using the sum of squared intensity differences as the similarity measure depends the image intensity knowledge absolutely. The assumption is that the intensities of two corresponding voxels are equal. However this condition is seldom fulfilled in real-world medical image registration without intensity normalization, because there are many factors that may affect observed intensities of a tissue over the imaged field, such as the different scanner or scanning parameters, normal aging, different subjects, and so on. In addition, there are large differences between individuals, which will influence the quality of inter-subject registration. Spatial normalization should also be done in advance. Combined with Demons algorithm, a step-wise non-rigid registration strategy is proposed. The proposed method not only can perform intensity and spatial normalization simultaneously, but also has higher localization ability.
3. Propose a topology preserved Demons non-rigid registration algorithm.
One disadvantage of Demons algorithm is that the topological invariance can not be ensured in theory, though bijectivity and deformation field smoothing technologies are adopted. The obtained deformation field is a dense vector field, which has a high deformation freedom. If there are no constraints to the deformation field, the spatial transformation can not be guaranteed reasonable and realizable. Through analyzing the critical points of a vector field, a topology preserved Demons non-rigid registration algorithm is proposed. In the context of preserving the characteristics as much as possible, the original deformation field changes to be topology preserved.
4. Propose a hybrid intensity and shape features non-rigid registration algorithm.
The deep brain internal structures, such as the caudate nucleus, putamen, thalamus, hippocampus, have close relationships with Parkinson's syndrome, epilepsy, dementia, Creutzfeldt-Jakob and other brain diseases. Analyzing the size and shape changes will contribute to the clinical diagnosis. However, these structures have complex shapes, small sizes, fuzzy boundaries and large partial volume effect in MRI images. Therefore, segmenting deep brain internal structures is a challenging task. Among different segmentation methods, registration based segmentation method is promising. By analyzing basic principles and the weakness of the intensity based non-rigid registration algorithm, a new hybrid intensity and shape features non-rigid registration algorithm is proposed. It is suitable for multi-object segmentation problem. Better results are obtained when the proposed algorithm is used to segment deep brain internal structures.
人体结构的复杂性使得简单的刚性配准无法表示人体复杂结构的局部细节,而这些细节却是临床诊断的重要依据,因此有必要采用具有更高自由度的非刚性配准技术。同刚性配准相比,非刚性配准还不成熟,如何建立合理的形变模型,如何提高非刚性配准的速度、精度以及对非刚性配准的评估都需要进一步的研究。
在目前的非刚性配准技术中,基于物理模型的非刚性配准以其特有的描述组织自然行为的能力,成为该领域中的一个重要分支。本文在深入研究基于热力学扩散模型的Demons非刚性配准技术的基础上,就灰度与空间标准化、变形场的正则化、先验知识对配准算法的性能影响等问题展开了研究,并提出了具有拓扑保持性的适用于单模态图像配准的混合灰度与特征信息的非刚性配准算法,然后将其应用到3D脑深层灰质结构分割中。具体成果介绍如下：
1.分析、优化并实现了基于扩散模型的]Demons非刚性配准算法。
基于物理模型的配准是一类重要的非刚性配准技术,其中Demons非刚性配准是该类方法中适用于单模态图像配准的一个优秀代表。本文对Demons算法及其重要的改进算法-主动Demons算法进行了深入细致的分析和算法实现,重点研究了该算法的两个重要参数,弹性系数和均化系数,及其形变驱动力的局限性,得出了实际应用时参数设置的一般性原则,并给出了改进的形变驱动力。
2.提出了同时完成灰度与空间标准化的非刚性配准算法。
以两图像灰度差的平方和作为衡量图像相似程度的非刚性配准算法完全依赖图像的灰度信息,其假设前提是两图像中对应像素点的灰度相同。然而,对于MRI图像,受不同扫描仪器、不同参数设置以及不均匀场、干扰、噪声等影响,难以满足这样的假设条件,需要进行灰度匹配预处理。另外,对于不同个体间的配准问题,不同个体的生理差异较大。过大的图像空间位置差异也会影响非刚性配准技术的配准质量,因此需要预先进行空间标准化处理。本文研究了非刚性配准的灰度和空间标准化问题,结合Demons算法,提出了一种分步配准策略,不仅能够完成灰度和空间的同步标准化,而且具有较高的描述局部微小形变的能力。
3.提出了具有拓扑保持性的Demons非刚性配准算法。
Demons算法的一个主要缺陷是不能从理论上保证配准得到的空间变换具有拓扑保持性。尽管该算法试图通过双射和变形场平滑技术得到具有拓扑不变性的空间变换,然而其可靠性未能得到理论证明和实验支持。该算法得到的是一个稠密的变形场,图像具有很高的变形自由度。高自由度意味着有更多的像素可以自由运动,如果没有附加约束,不能保证得到的空间变换是合理的,可以实现的。因此本文重点研究了变形场的约束问题,通过分析矢量场关键点特性,提出了在尽量保持原有变形场几何特性的前提下校正原变形场拓扑性的非刚性配准算法,并通过实验对算法性能进行了评价。
4.提出了基于灰度与形状混合信息的非刚性配准算法。
人脑的深层灰质结构,如尾状核、壳体、苍白球、丘脑、海马体等等,与帕金森氏综合征、癫痫、痴呆、克雅氏病等脑部疾病关系密切,通过分析这些结构的体积、形状变化等有助于相关的疾病诊断。然而这些结构形状复杂,体积小,在核磁共振图像中,它们的边界模糊,受部分容积效应的影响大。因此,深层脑结构的全自动分割是一个具有挑战性的问题。在已有的研究中,基于配准技术的分割是解决该问题一种有效手段。本文通过分析基于灰度的非刚性配准算法的基本原理和不足,在配准能量函数中引入形状相似度项,提出了新的灰度信息与形状信息相结合的非刚性配准算法。算法适用于相似灰度多目标分割问题,在分割体积小、形状复杂、边缘模糊的深层脑结构的应用中得到了较好的结果。
Image segmentation plays an important role in medical image analysis. Medical image segmentation aims to divide an entire image into different non-overlapping regions. These regions represent different anatomical structures clearly. Segmenting and analyzing anatomical structures from brain MRI images is an effective method to diagnose brain diseases and track their evolvement. However the complexities of medical images make medical image segmentation a challenging task. Fortunately, the a priori knowledge provided by the atlas can contribute much to a better segmentation. Image registration is a good technique to fuse medical priori knowledge into segmentation procedure, which forms a new image segmentation technique, registration based segmentation. In registration based segmentation, an optimal spatial transformation deforming the source image into the target image is first obtained. Then the labeled structures in the atlas (correspondence to the source image) are mapped to the target image based on the obtained optimal spatial transformation. Finally, the required segmentation will be acquired easily. Obviously, the segmentation quality mainly depends on the registration quality.
Using simple rigid registration to describe subtle local differences between individuals is insufficient due to the complexity of the human body structures. Therefore non-rigid registration techniques should be used to solve the problem. Non-rigid registration approaches usually have higher degree of freedom, and can describe more complex nonlinear deformations. Compared to the rigid registration, non-rigid registration is not mature. There are many problems to be solved, such as establishing a reasonable deformation model, improving the registration speed and accuracy, developing convincing algorithm evaluation criteria, and so on.
Non-rigid registration based on physical models is an important branch in the current techniques, because they have unique abilities to describe the physical behaviors of organism. In this dissertation, we studied diffusion model based Demons non-rigid registration algorithm in depth, which originated from thermodynamic theory. The main researches focus on the intensity and spatial normalization, regularization method of the deformation field, the contributions of a priori knowledge to non-rigid registration and image segmentation. In segmenting deep brain internal structures applications, a hybrid intensity and shape knowledge non-rigid registration algorithm is proposed. The major contributions of this dissertation are as follows:
1. Analyze, optimize, and implement diffusion model based Demons non-rigid registration algorithm.
Demons non-rigid registration is one of excellent algorithms in physical model based registration. It is suitable to register two images within the same modality. In this dissertation, the Demons algorithm and its variant, active Demons algorithm, are studied in depth. Two important parameters, elasticity parameter and equalization parameter, and the limitations of the deformation force are analyzed carefully. Then instructive parameter setting principles and an improved deformation force are proposed.
2. Propose a more localized non-rigid registration algorithm, which can complete spatial and intensity normalization simultaneously.
Non-rigid registration algorithm using the sum of squared intensity differences as the similarity measure depends the image intensity knowledge absolutely. The assumption is that the intensities of two corresponding voxels are equal. However this condition is seldom fulfilled in real-world medical image registration without intensity normalization, because there are many factors that may affect observed intensities of a tissue over the imaged field, such as the different scanner or scanning parameters, normal aging, different subjects, and so on. In addition, there are large differences between individuals, which will influence the quality of inter-subject registration. Spatial normalization should also be done in advance. Combined with Demons algorithm, a step-wise non-rigid registration strategy is proposed. The proposed method not only can perform intensity and spatial normalization simultaneously, but also has higher localization ability.
3. Propose a topology preserved Demons non-rigid registration algorithm.
One disadvantage of Demons algorithm is that the topological invariance can not be ensured in theory, though bijectivity and deformation field smoothing technologies are adopted. The obtained deformation field is a dense vector field, which has a high deformation freedom. If there are no constraints to the deformation field, the spatial transformation can not be guaranteed reasonable and realizable. Through analyzing the critical points of a vector field, a topology preserved Demons non-rigid registration algorithm is proposed. In the context of preserving the characteristics as much as possible, the original deformation field changes to be topology preserved.
4. Propose a hybrid intensity and shape features non-rigid registration algorithm.
The deep brain internal structures, such as the caudate nucleus, putamen, thalamus, hippocampus, have close relationships with Parkinson's syndrome, epilepsy, dementia, Creutzfeldt-Jakob and other brain diseases. Analyzing the size and shape changes will contribute to the clinical diagnosis. However, these structures have complex shapes, small sizes, fuzzy boundaries and large partial volume effect in MRI images. Therefore, segmenting deep brain internal structures is a challenging task. Among different segmentation methods, registration based segmentation method is promising. By analyzing basic principles and the weakness of the intensity based non-rigid registration algorithm, a new hybrid intensity and shape features non-rigid registration algorithm is proposed. It is suitable for multi-object segmentation problem. Better results are obtained when the proposed algorithm is used to segment deep brain internal structures.
引文
[1]Jan Modersitzki. Numerical methods for image registration [M]. Oxford University Press, New York, 2004.
[2]Lisa Gottesfeld BROWN. A survey of image registration techniques[J]. ACM Computing Surveys, 1992,24(4):325-376.
[3]章毓晋.图像工程(第二版)[M].北京：清华大学出版社,2007.
[4]Lemieux L, Wieshmann UC Moran NF, Fish DR, Shorvon SD. The detection and significance of subtle changes in mixed-signal brain lesions by serial MRI scan matching and spatial normalization [J]. Medical Image Analysis,1998,2(3):227-242.
[5]Rao A, Chandrashekara R, Sanchez-Ortiz GI, et al. Spatial transformation of motion and deformation fields using nonrigid registration [J]. IEEE Transactions on Medical Imaging,2004,23 (9):1065-1076.
[6]Baete K, Nuyts J, Laere KV, et al. Evaluation of anatomy based reconstruction for partial volume correction in brain FDG-PET [J]. Neurolmage,2004,23(1):305-317.
[7]Lee J-M, Kim SH, Jang DP, et al. Deformable model with surface registration for hippocampal shape deformity analysis in schizophrenia [J]. NeuroImage,2004,22(2):831-840.
[8]'Richard FJP, Cohen LD. A new image registration technique with free boundary constraints: application to mammography[J]. Computer Vision and Image Understanding,2003,89(2-3):166-196.
[9]徐胜舟,宋恩民,许向阳.基于改进Demons算法的乳腺X线摄片非刚性配准[J].中国图象图形学报,2009,14(12)：2566-2571.
[10]杨健,王涌天,唐宋元,周寿军,刘越.基于互信息量和薄板样条的X射线造影图像弹性配准[J].电子学报.2007,35(1)：127-130.
[11]王君臣,王田苗,徐源,方礼明.基于ICP算法的手术导航三维配准技术[J].北京航空航天大学学报,2009,35(4)：434-438.
[12]葛云,章东.基于Legendre正交矩的配准方法在放疗导航中的应用研究[J].电子学报,2009,37(7)：1529-1531.
[13]陈芳,张建秋,胡波.基于块图像内容自适应选择的数字减影血管造影术的图像配准法[J].航天医学与医学工程,2009,22(1)：49-55.
[14]卢振泰.医学图像配准算法研究[D].博士学位论文,南方医科大学,2008.
[15]张少敏,支力佳,赵大哲,赵宏,杨金柱.基于区域联合Renyi熵的多模医学图像配准[J].电子学报,20·09,37(10)：2320-2325.
[16]刘晴,郭希娟,许慎洋.基于互信息的N维多模医学图像配准[J].中国图象图形学报,2009,14(10)：2061-2068.
[17]张加万,李谭,孙济洲.基于Dividing Rectangles的多模态医学图像配准算法[J].中国图象图形学报,2008,13(4)：749-755.
[18]何元烈,田联房,陈萍,王立非,叶广春,毛宗源.基于三次样条层间插值的多模态医学图像配准[J].生物医学工程学杂志,2007,24(6)：1241-1245.
[19]J. B. Antoine Maintz, Max A. Viergever. A survey of medical image registration [J]. Medical Image Analysis,1998,2(1):1-36.
[20]A. Ardeshir Goshtasby.2-D and 3-D image registration:for medical, remote sensing, and industrial applications[M]. A John Wiley & Sons, Inc., Publication,2005.
[21]Barbara Zitova, Jan Flusser. Image registration methods:a survey[J]. Image and Vision Computing, 2003,21:977-1000.
[22]Mei Chen.3-D deformable registration using a statistical atlas with applications in medicine [D]. Doctoral thesis, The Robotics Institute Carnegie Mellon University,1999.
[23]Ali Gholipour, Nasser Kehtarnavaz, Richard Briggs, et al. Brain functional localization:a survey of image registration techniques [J]. IEEE Transactions on Medical Imaging,2007,26 (4):427-451.
[24]Isaac H. Bankman. Handbook of medical image processing and analysis (second edition) [M]. Academic Press, USA,2009.
[25]刘新刚,陈武凡,陈光杰.基于先验知识和MRF随机场模型的医学图像弹性配准方法[J].中国生物医学工程学报.2006,25(2)：151-157.
[26]彭晓明,陈武凡,马茜.基于B样条的弹性点配准方法[J].中国图象图形学报,2007,12(6)：1079-1085.
[27]吴月娥,王小喜,哈章,李传富,周平,周康源.基于薄板样条和形状内容的医学图像非刚性配准方法研究[J].航天医学与医学工程,2007,20(1)：43-46.
[28]李静,杨烜,喻建平.基于特征分类的MRI医学图像弹性配准[J].中国图象图形学报,2007,12(6)：1069-1078.
[29]龚永义,罗笑南,贾维嘉,黄厚生.基于改进的弹簧质子模型的医学图像配准[J].计算机学报,2008,31(7)：1224-1233.
[30]廖秀秀,于慧敏,扬威.基于隐含形状表示和边缘信息融合的非刚体图像配准[J].中国生物医学工程学报,2008,27(3)：340-346.
[31]荣成城,周健,曹国刚,罗立民.基于粘滞流体B样条模型的快速非刚体配准方法[J].中国图象图形学报,2009,14(4)：712-717.
[32]J. L. Lancaster, T. G. Glass, B. R. Lankipalli, et al. A modality independent approach to spatial normalization of tomographic images of the human brain[J]. Hum. Brain Mapp.,1995,3:209-223.
[33]Y. Han and H. Park. Automatic registration of brain magnetic resonance images based on Talairach reference system [J]. J. Magn. Reson. Imag.,2004,20(10):572-580.
[34]K. Rohr, M. Fornefett, H. S. Stiehl. Spline-based elastic image registration:integration of landmark errors and orientation attributes [J]. Comput. Vis. Image Understanding,2003,90:153-168.
[35]K. Rohr, H. S. Stiehl, R. Sprengel, et al. Landmark-based elastic registration using approximating thin plate splines [J]. IEEE Trans. Med. Imag.,2001,20(6):526-534.
[36]M. Xu, W. L. Nowinski. Talairach-Toumoux brain atlas registration using a metal forming principle-based finite element method [J]. Med. Imag. Anal.,2001,5:271-279.
[37]Pielot R, Scholz M, Obermayer K, et al. A new point-based warping method for enhanced and simplified analysis of functional brain image data [J]. Neurolmage,2003,19(4):1716-1729.
[38]Xue Z, Shen D, Davatzikos C. Determining correspondence in 3-D MR brain images using attribute vectors as morphological signatures of voxels [J]. IEEE Transactions on Medical Imaging,2004,23(10): 1276-1291.
[39]Bookstein FL, Green WDK. A Feature space for edgels in images with landmarks [J]. J. Math. Imaging and Vision,1993,3:231-261.
[40]Mardia KV, Little JA. Image warping using derivative information [C]. Math. Methods in Med. Imaging Ⅲ, SPIE Vol.1994,2299:16-31.
[41]Joshi SC, Miller MI, Christensen GE, et al. Hierarchical brain mapping via a generalized dirichlet solution for mapping brain manifolds [C]. Vision Geometry Ⅳ, Proc. SPIE Conference on Optical Science, Engineering and Instrumentation, San Diego, CA, August.1995,2573:278-289.
[42]Ge Y, Fitzpatrick JM, Kessler RM, Jeske-Janicka M. Intersubject brain image registration using both cortical and subcortical landmarks [C]. SPIE Image Processing,1995,2434:81-95.
[43]P. Cachier, J.-F. Mangin, X. Pennec, et al. Multisubject non-rigid registration of brain MRI using intensity and geometric features [C]. Lecture Notes in Computer Science. Berlin, Germany: Springer-Verlag,2001,2208:734-742.
[44]E. Hellier, C. Barillot. Coupling dense and landmark-based approaches.for non-rigid registration [J]. IEEE Trans. Med. Imag.,2003,22(2):217-227.
[45]Declerck J, Subsol G, Thirion JP. Automatic retrieval of anatomical structures in 3D medical images [R]. INRIA Technical Report 2485,1995.
[46]Feldmar J, Declerck J, Malandain G, Ayache N. Extension of the ICP algorithm to nonrigid intensity-based registration of 3D volumes [J]. Computer Vision and Image Understanding,1997,66(2): 193-206.
[47]Cac Z, Pan S, Li R, et al. Registration of medical images using an interpolated closest point transform: method and validation [J]. Medical Image Analysis,2004,8(4):421-427.
[48]Liu Y. Improving ICP with easy implementation for free-form surface matching [J]. Pattern Recognition,2004,37(2):211-226.
[49]Thompson P, Toga AW. A surface-based technique for warping three-dimensional images of the brain [J]. IEEE Transactions on Medical Imaging,1996,15(4):402-417.
[50]D. Shen, C. Davatzikos. HAMMER:Hierarchical attribute matching mechanism for elastic registration [J]. IEEE Trans. Med. Imag.,2002,21(11):1421-1439.
[51]D. Shen. Image registration by hierarchical matching of local spatial intensity histograms [C]. Lecture Notes in Computer Science. Berlin, Germany:Springer-Verlag,2004,3216:582-590.
[52]G. Wu, F. Qi, D. Shen. Learning best features for deformable registration of MR brains [C]. Lecture Notes in Computer Science. Berlin, Germany:Springer-Verlag,2005,3750:179-187.
[53]G. Wu, F. Qi, D. Shen. Learning-based deformable registration of MR brain images [J]. IEEE Trans. Med. Imag.,2006,25(9):1145-1157.
[54]T. Liu, D. Shen, C. Davatzikos. Deformable registration of cortical structures via hybrid volumetric and surface warping [C]. Lecture Notes in Computer Science. Berlin, Germany:Springer-Verlag,2003, 2879:780-787.
[55]T. Liu, D. Shen, C. Davatzikos. Deformable registration of tumor-diseased brain images [C]. Lecture Notes in Computer Science. Berlin, Germany:Springer-Verlag,2004,3216:720-728.
[56]Z. Xue, D. Shen, C. Davatzikos. Correspondence detection using wavelet-based attribute vectors [C], Lecture Notes in Computer Science. Berlin, Germany:Springer-Verlag,2003,2879:762-770.
[57]Z. Xue, D. Shen, C. Davatzikos. Statistical representation of high-dimensional deformation fields with application to statistically constrained 3D warping [J]. Med. Image Anal.,2006,10:740-751.
[58]B. C. Vemuri, J. Ye, Y. Chen, C. M. Leonard. Image registration via level-set motion:applications to atlas-based segmentation [J]. Med. Image Anal.,2003,7:1-20.
[59]Y.Wang, L. H. Staib. Physical model-based non-rigid registration incorporating statistical shape information [J]. Med. Image Anal.,2000,4:7-20.
[60]Cohen LD, Cohen I. Deformable models for 3D medical images using finite elements & balloons [C]. 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Los Alamitos, CA, USA. IEEE Comput. Soc. Press,1992:592-598.
[61]A. Kassam, M. L. Wood. Fourier registration of three-dimensional brain MR images:exploiting the axis of rotation [J]. Journal of Magnetic Resonance Imaging,1996,6:894-902.
[62]Amit Y, Grenander U, Piccioni M. Structural image restoration through deformable templates [J]. J. Amer. Stat. Assoc,1991,86(414):376-387.
[63]Bookstein FL. Principal warps:thin-plate splines and the decomposition of deformations [J] IEEE Trans. Pattern Analysis and Machine Intelligence,1989,11(6):567-585.
[64]Bookstein FL. Proposal for a biometrics of the cortical surface:a statistical method for relative surface distance metrics [C]. Proc. of the SPIE Conference on Vision Geometry,1995,2573:312-323.
[65]Bookstein FL. Landmark methods for forms without landmarks:morphometrics of group differences in outline shape [J]. Medical Image Analysis,1997,1(3):225-243.
[66]Collins DL, Holmes CJ, Peters TM, Evans AC. Automatic 3D model-based neuroanatomical segmentation [J]. Human Brain Mapping,1995,3:190-208.
[67]S. Joshi, B. Davis, M. Jomier, G. Gerig. Unbiased diffeomorphic atlas construction for computational anatomy [J]. Neurolmage,2004,23:S151-S160.
[68]J. D. Gispert, J. Pascau, S. Reig, et al. Influence of the normalization template on the outcome of statistical parametric mapping of PET scans [J]. NeuroImage,2003,19:601-612.
[69]J. Ashburner, K. Friston. Multimodal image coregistration and partitioning-A unified framework [J]. NeuroImage,.1997,6:209-217.
[70]Thirion J P. Image matching as a diffusion process:an analogy with Maxwell's demons [J]. Medical Image Analysis,1998,2(3):243-260.
[71]J. Suckling, C. Long, C. Triantafyllou, et al. Variable precision registration via wavelets:optimal spatial scales for inter-subject registration of functional MRI [J]. Neuroimage,2006,31:197-208.
[72]R. Stefanescu, X. Pennec, N. Ayache. Grid powered nonlinear image registration with locally adaptive regularization [J]. Med. Image Anal.,2004,8:325-342..
[73]林相波,邱天爽,Frederic Nicolier,RUAN Su.Active Demons可变形图像配准算法研究[J].中国生物医学工程学报.2008,27(4)：636-640.
[74]J. Ashburner, J. L. R. Andersson, K. J. Friston. Image registration using a symmetric prior-in three dimensions [J]. Hum. Brain Mapp.,2000,9:212-225.
[75]A. Cuzol, P. Hellier, E. Memin. A novel parametric method for non-rigid image registration [C]. Lecture Notes in Computer Science. Berlin, Germany:Springer-Verlag,2005,3565:456-467.
[76]P. Hellier, J. Ashburner, I. Corouge, et al. InterSubject registration of functional and anatomical data using SPM [C]. Lecture Notes in Computer Science. Berlin, Germany:Springer-Verlag,2002,2489: 590-597.
[77]E.Hellier, C. Barillot, E. Memin, P. Perez. Hierarchical estimation of a dense deformation field for 3-D robust registration [J]. IEEE Trans. Med. Imag.,2001,20(5):388-402.
[78]G. E. Christensen, H. J. Johnson. Consistent image registration [J]. IEEE Trans. Med. Imag.,2001, 20(7):568-582.
[79]F. Crivello, T. Schormann, N. Tzourio-Mazoyer, et al. Comparison of spatial normalization procedures and their impact on functional maps [J]. Hum. Brain Mapp.,2002,16:228-250.
[80]Studholme C, Hill DLG, Hawkes DJ. Automated registration of truncated MR and CT datasets of the head [C]. Proc. Br. Mach. Vision Conf.,1995, pp:27-36.
[81]Collignon A, Maes F, Delaere D, et al. Automated multi-modality image registration based on information theory [M]. Information Processing in Medical Imaging, Bizais Y, Barillot C, Di Paola R, (eds.), pp.263-274, Dordrecht, The Netherlands:Kluwer Academic,1995.
[82]Maes F, Collignon A, VandermeulenD, et al. Multimodality image registration by maximization of mutual information [J]. IEEE Trans. Med. Imaging,1997,16:187-198.
[83]Wells Ⅲ WM, Viola P, Atsumi H, et al. Multi-modal volume registration by maximization of mutual information [J]. Med. Image Anal.,1996,1:35-51.
[84]D. Seghers, E. D'Agostino, F. Maes, et al. Construction of a brain template from MR images using state-of-the-art registration and segmentation techniques [C]. Lecture Notes in Computer Science. Berlin, Germany:Springer-Verlag,2004,3216:696-703.
[85]Q. Wang, D. Seghers, E. D'Agostino, et al. Construction and validation of mean shape atlas templates for atlas-based brain image segmentation [C]. Lecture Notes in Computer Science. Berlin, Germany: Springer-Verlag,2005,3565:689-700.
[86]M. De Craene, A. du Bois d'Aische, B. Macq, S. K. Warfield. Multi-subject variational registration for probabilistic unbiased atlas generation [C]. Proc. ICIP 2005:IEEE Int. Conf. Image Processing, Sep.2005, 3:111-601-Ⅲ-604.
[87]M.Jenkinson, S. Smith. A global optimisation method for robust affme registration of brain images [J]. Med. Imag. Anal.,2001,5:143-156.
[88]G. K. Rohde, A. Aldroubi, B. M. Dawant. The adaptive bases algorithm for intensity-based nonrigid image registration [J]. IEEE Trans. Med. Imag.,2003,22(11):1470-1479.
[89]E.D'Agostino, F. Maes, D.Vandermeulen, P. Suetens. Non-rigid atlas-to-image registration by minimization of class-conditional image entropy [C]. Lecture Notes in Computer Science. Berlin, Germany: Springer-Verlag,2004,3216:745-753.
[90]J. Ashburner, J. L. R. Andersson, K. J. Friston. High-dimensional image registration using symmetric priors [J]. NeuroImage,1999,9:619-628.
[91]衣文文,常发亮,胡彦磊.一种基于互信息的三级医学图像配准策略[J].航天医学与医学工程,2008,21(6)：518-522.
[92]张泾周,李婷,张佳.基于广义互信息的人脑图像配准研究[J].生物医学工程学杂志,2008,25(6)：1303-1306.
[93]K. K. Bhatia, J. V. Hajnal, B. K. Puri, et al. Consistent groupwise non-rigid registration for atlas construction [C]. Proc. IEEE Int. Symp. Biomedical Imaging:Macro to Nano, Apr.2004,1:908-911.
[94]D. Rueckert, A. F. Frangi, J. A. Schnabel. Automatic construction of 3-D statistical deformation models of the brain using nonrigid registration [J]. IEEE Trans. Med. Imag.,2003,22(8):1014-1025.
[95]C. Studholme, V. Cardenas, E. Song, et al. Accurate template-based correction of brain MRI intensity distortion with application to dementia and aging [J]. IEEE Trans. Med. Imag.,2004,23(1):99-110.
[96]P. Lorenzen, M. Prastawa, B. Davis, et al. Multi-modal image set registration and atlas formation [J]. Med. Imag. Anal.,2006,10:440-451.
[97]E.D'Agostino, F. Maes, D.Vandermeulen, P. Suetens. An information theoretic approach for non-rigid image registration using voxel class probabilities [C]. Lecture Notes in Computer Science. Berlin, Germany: Springer-Verlag,2003,2879:812-820.
[98]P. J. Lorenzen, S. C. Joshi. High-dimensional multi-modal image registration [C]. Lecture Notes in Computer Science. Berlin, Germany:Springer-Verlag,2003,2717:234-243.
[99]R. P. Woods, S. R. Cherry, J. C. Mazziotta. Rapid automated algorithm for aligning and reslicing PET images [J]. J. Comput. Assist. Tomogr.,1992,16:620-633.
[100]R. P. Woods, S. T. Grafton, C. J. Holmes, et al. Automated image registration:I. General methods and intrasubject, intramodality validation [J]. J. Comput. Assist. Tomogr,1998,22:139-152.
[101]R. P. Woods, J. C. Mazziotta, S. R. Cherry. MRI-PET registration with automated algorithm [J]. J. Comput. Assist. Tomogr.,1993,17:536-546.
[102]H. Lester, S. R. Arridge. A survey of hierarchical non-linear medical image registration [J]. Pattern Recognition,1999,32(1):129-149.
[103]C. Broit. Optimal registration of deformed images [D]. Ph.D. dissertation, Dept. Comput. Inf. Sci., Univ. Pennsylvania, Philadelphia,1981.
[104]R. Bajcsy, R. Lieberson, M. Reivich. A computerized system for the elastic matching of deformed radiographic images to idealized atlas images [J]. J. Comput. Assist. Tomogr.,1983,7(4):618-625,.
[105]J. C. Gee, D. R. Haynor, M. Reivich, R. Bajcsy. Finite element approach to warping of brain images [C]. SPIE Med. Imag.,1994,2167:327-337.
[106]R. Bajcsy, S. Kovacic. Multiresolution elastic matching [J]. Comput. Vision Graph. Image Process., 1989,46(1):1-21.
[107]C. Davatzikos, R. N. Bryan. Using a deformable surface model to obtain a shape representation of the cortex [J]. IEEE Trans. Med. Imag.,1996,15(6):785-795.
[108]C. Davatzikos. Spatial transformation and registration of brain images using elastically deformable models [J]. Comput. Vision Image Understand.,1997,66:207-222.
[109]G. E. Christensen, R. D. Rabbitt, M. I. Miller.3D brain mapping using a deformable neuroanatomy [J]. Phys. Medicine Biol.,1994,39:609-618.
[110]G. E. Christensen, R. D, Rabbitt, M. I. Miller. Deformable templates using large deformation kinematics [J]. IEEE Trans. Image Process.,1996,5:1435-1447.
[111]G. E. Christensen, S. C. Joshi, M. I. Miller. Volumetric transformation of brain anatomy [J]. IEEE Trans. Med. Imag.,1997,16(6):864-877.
[112]M. Bro-Nielsen, C. Gramkow. Fast fluid registration of medical images [C]. Proc. Visualization in Biomedical Computing, R. Kikinis and K. Hoehne, Eds. Berlin, Germany:Springer Verlag,1996, pp. 267-276.
[113]Thirion, J.P.. Fast non-rigid matching of 3D medical images [R], Technical report 2547, INRIA, 1995.
[114]Meritxell Bach Cuadra. Atlas-based segmentation and classification of magnetic resonance brain images [D]. Ph.D thesis, Ecole Polytechnique Federale De Lausanne, Switzerland,2003.
[115]P. Cachier, X. Pennec, N. Ayache. Fast non rigid matching by gradient descent:study and improvements of the "Demons" algorithm [R]. Technical Report, INRIA 3706,1999.
[116]He Wang, Lei Dong, J O'Daniel, et al. Validation of an accelerated'demons'algorithm for deformable image registration in radiation therapy [J]. Phys. Med. Biol.,2005,50(12):2887-2905.
[117]Xavier Pennec, Pascal Cachier, Nicholas Ayache. Understanding the demon's algorithm:3D non-rigid registration by gradient descent [C]. Proceedings of the 2nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI'99), Lecture Notes in Computer Science, Springer-Verlag,1999,1679:597-605.
[118]Pascal Cachier, Eric Bardinet, Didier Dormont, et al. Iconic feature based nonrigid registration:The PASHA algorithm [J]. Computer vision and image understanding,2003,89(2-3):272-298.
[119]Tom Vercauteren, Xavier Pennec, Aymeric Perchant, Nicholas Ayache. Non-parametric diffeomorphic image registration with the demons algorithm [C]. Proceedings of the 10th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI'07), Brisbane, Australia, October 2007, Lecture Notes in Computer Science, Springer-Verlag,4792:319-326.
[120]XiiangBo Lin, Su Ruan, Frederic Morain-Nicolier, TianShuang Qiu. Non-rigid registration based segmentation of brain subcortical structures using a priori knowledge [C]. The proceedings of the 30th Annual International IEEE EMBS Conference. Vancouver, British Columbia, Canada, August 20-24,2008, pp:3971-3974.
[121]Xiangbo LIN, Tianshuang QIU, Frederic Nicolier, Su RUAN. An improved method of "Demons" non-rigid image registration algorithm [C]. The proceedings of the 9th International Conference on Signal Processing, Beijing China, October 26-29,2008, pp:1091-1094.
[122]A. Guimond, A. Roche, N. Ayache, J. Meunier. Three-dimensional multimodal brain warping using the demons algorithm and adaptive intensity corrections[J]. IEEE Trans. Med. Imag.,2001,20(1):58-69.
[123]J. D. Humphrey. Continuum biomechanics of soft biological tissues [C]. Proc. R. Stat. Soc.,2003, 459(2029):3-46.
[124]Mark Holden. A review of geometric transformation for nonrigid body registration [J]. IEEE Trans. Med. Imag.,2008,27(1):111-128.
[125]R. P. Woods, S. T Grafton, J. D. G. Watson, et al. Automated image registration:Ⅱ. intersubject validation of linear and nonlinear models [J]. Journal of Computer Assisted Tomography,1998, 22(1):153-165.
[126]R Szeliski, S. Lavall'ee. Matching 3-D anatomical surfaces with non-rigid deformations using octree-splines [C]. IEEE Workshop on Biomedical Image Analysis,1994, pp:144-153.
[127]J. Ashburner, K. J. Friston. Nonlinear spatial normalization using basis functions [J]. Human Brain Mapping,1999,7:254-266.
[128]Y. Amit. A nonlinear variational problem for image matching [J]. SIAM Journal on Scientific Computing,1994,15(1):207-224.
[129]S. Gefen, E. Tretiak, J. Nissanov. Elastic 3D alignment of rat brain histological images [J]. IEEE Trans. Med. Imag.,2003,22(11):1480-1489.
[130]Y. T. Wu, L. F. Chen, P. L. Lee, et al. Discrete signal matching using coarse-to-fine wavelet basis functions [J]. Pattern Recognition,2003,36(1):171-192.
[131]M. Fornefett, K. Rohr, H. S. Steihl. Radial basis functions with compact support for elastic registration of medical images [J]. Image Vis:Comput,2001,19(1-2):87-96.
[132]I. Barrodale, D. Skea, M. Berkley, et al. Warping digital images using thin plate splines [J]. Pattern Recognition,1993,26:375-376.
[133]J. Kohlrausch, K. Rohr, H. S. Stiehl. A new class of elastic body splines for nonrigid registration of medical images [J]. J. Math. Imag. Vis.,2005,23(3):253-280.
[134]S. W. K. Rohr. Physics-based elastic image registration using splines and including landmark localization uncertainties [C]. Med. Imag., Comput. Computer-Assisted Intervention-MICCAF 06,2006, (2):678-685.
[135]D. Rueckert, L. I. Sonoda, C. Hayes, et al. Nonrigid registration using free-form deformations: application to breast MR images [J]. IEEE Trans. Med. Imag.,1999,18(8):712-721.
[136]J. M. Declerck, J. Feldmar, M. L. Goris, F. Betting. Automatic registration and alignment on a template of cardiac stress and rest reoriented SPECT images [J]. IEEE Trans. Med. Imag.,1997,16(6): 727-737.
[137]Rueckert, D., Aljabar, P., Heckemann, et al. Diffeomorphic registration using B-splines [C].9th International Conference on Medical Image Computing and Computer-Assisted Intervention:MICCAI, 2006,4191:702-709.
[138]P. Thevanaz, T. Blu, M. Unser. Interpolation revisited [J]. IEEE Trans. Med. Imag.,2000,19(7): 739-758.
[139]M. W. Hsu, J. F. Hughes, H. Kaufman. Direct manipulation of free-form deformations [C]. Proc. SIGGRAPH,1992,6:177-184.
[140]J. Kybic, M. Unser. Fast parametric elastic image registration [J]. IEEE Trans. Image Process.,2003, 12(11):1427-1442.
[141]E.D'Agostino, F. Maes, D.. An information theoretic approach for non-rigid image registration using voxel class probabilities [J]. Med. Image Anal.,2006,10:413-431.
[142]S. Ourselin, A. Roche, S. Prima,]N. Ayache. Block matching:a general framework to improve robustness of rigid registration of medical images [C]. Lecture Notes in Computer Science. Berlin, Germany:Springer-Verlag,2000,1935:557-566.
[143]V. A. Magnotta, H. J. Bockholt, H. J. Johnson, et al. Subcortical, cerebellar, and magnetic resonance based consistent brain image registration [J]. Neurolmage,2003,19:233-245.
[144]Arno Klein, Jesper Andersson, Babak A. Ardekani, et al. Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration [J]. Neurolmage,2009,46:786-802.
[145]A. Tikhonov, V. Arsenin. Solutions of ill-posed problems [M]. Winston and Salem, Washington, DC, 1977.
[146]Meritxell Bach Cuadra. Atlas-based segmentation and classification of magnetic resonance brain images [D]. Ph.D thesis,2003, Ecole Polytechnique Federale De Lausanne, Switzerland.
[147]L. G. Nyul, J. K. Udupa, X. Zhang. New variants of a method of MRI scale standardization [J]. IEEE Trans. Med. Imag.,2000,19(2):143-150.
[148]Y. Ge, J. K. Udupa, L. G. Nyul, et al. Numerical tissue characterization in MS via standardization of the MR image intensity scale [J]. J. Magn. Reson. Imag.,2000,12(5):715-721.
[149]P. Hellier. Consistent intensity correction of MRimages [C]. Int. Conf. Image Process. (ICIP 2003), Sept.2003,1:1109-1112.
[150]V. Noblet, C. Heinrich, F. Heitz, et al. Retrospective evaluation of a topology preserving non-rigid registration method [J]. Medical Image Analysis,2006,10:366-384.
[151]Senthil Periaswamy, Hany Farid, Elastic registration in the presence of intensity variations [J]. IEEE Transactions on Medical Imaging,2003,22 (7):865-874.
[152]B.K.P. Horn. Robot Vision [M]. MIT press, Cambridge, MA,1986.
[153]Ibanez, L., Schroeder, W., Ng, L., Cates, J.. The ITK software guide:the insight segmentation and registration tool [R],2005. Kitware Inc. (http://www.itk.org).
[154]Daniel Schwarz, Tomas Kasparek, Ivo Provaznik, Jiri Jarkovsky. A deformable registration method for automated morphometry of MRI brain images in neuropsychiatric research [J]. IEEE Transactions on Medical Imaging,2007,26(4):452-461.
[155]R. Kikinis, M.E. Shenton, D.V. Iosifescu, et al. A digital brain atlas for surgical planning, model driven segmentation and teaching [J], IEEE Trans. Visual. Comput. Graph.,1996,2 (3):232-241.
[156]Internet Brain Segmentation Repository [DB]. M. G. H. Tech. Rep., Ctr. Morphometric Anal. IBSR, 2000 [Online].(http://neurowww.mgh.harvard.edu/cma/ibsr)
[157]John Ashburner. A fast diffeomorphic image registration algorithm [J]. Neurolmage,2007,38: 95-113.
[158]B.B. Avants, C.L. Epstein, M. Grossman, J.C. Gee. Symmetric diffeomorphic image registration with cross-correlation:evaluating automated labeling of elderly and neurodegenerative brain [J]. Medical Image Analysis,2008,12:26-41.
[159]Deshan Yang, Hua Li, Daniel A Low, et al. A fast inverse consistent deformable image registration method based on symmetric optical flow computation [J]. Phys. Med. Biol.,2008,53:6143-6165.
[160]Alex D. Leow, Igor Yanovsky, Ming-Chang Chiang, et al. Statistical properties of Jacobian maps and the realization of unbiased large-deformation nonlinear image registration [J]. IEEE Transactions On Medical Imaging,2007,26(6):822-832.
[161]Tom Vercauteren, Xavier Pennec, Aymeric Perchant, Nicholas Ayache. Diffeomorphic demons: efficient non-parametric image registration [J]. NeuroImage,2009,45:S61-S72.
[162]Alvarez L, Deriche R, Papadopoulo T, Javier Sanchez. Symmetrical dense optical flow estimation with occlusions detection [J]. Int. J. Comput. Vis.,2007,75:371-385.
[163]Leow A, Huang S-C, Geng A, et al. Inverse consistent mapping in 3D deformable image registration: its construction and statistical properties [J]. Information Processing in Medical Imaging (Berlin:Springer). 2005,3565:493-503.
[164]Dupuis P, Grenander U, Miller M I. Variational problems on flows of diffeomorphisms for image matching [J]. Quarterly of Applied Mathematics,1998,56:587-600.
[165]Christensen, G.E., Rabbitt, R.D., Miller, M.I..3D brain mapping using a deformable neuroanatomy [J]. Phys. Med. Biol.,1994,39:609-618.
[166]M. I. Miller, A. Trouve, L. Younes. On the metrics and Euler-Lagrange equations of computational anatomy [J]. Annu. Rev. Biomed. Eng.,2002,4:375-405.
[167]Beg, M.F., Miller, M.I., Trouve, A., Younes, L.. Computing large deformation metric mappings via geodesic flows of diffeomorphisms [J]. Int J. Comput. Vis.,2005,61(2):139-157.
[168]Avants B, Gee J. Symmetric geodesic shape averaging and shape interpolation [J]. Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis,2004,3117:99-110.
[169]Beg M F, Khan A. Symmetric data attachment terms for large deformation image registration [J]. IEEE Trans. Med. Imaging,2007,26:1179-1189.
[170]Musse O, Heitz F, Armspach J P. Topology preserving deformable image matching using constrained hierarchical parametric models [J]. IEEE Transactions on Image Processing,2001,10(7):1080-1093.
[171]Karacah B, Davatzikos C. Estimating topology preserving and smooth displacement fields [J]. IEEE Transactions on Medical Imaging,2004,23(7):868-880.
[172]Noblet V, Heinrich C, Heitz F, Armspach J P.3-D deformable image registration:A topology preservation scheme based on hierarchical deformation models and interval analysis optimization [J]. IEEE Transactions on Image Processing,2005,14(5):553-566.
[173]Michael Sdika. A Fast Nonrigid Image Registration With Constraints on the Jacobian Using Large Scale Constrained Optimization [J]. IEEE Transactions On Medical Imaging,2008,27(2):271-281.
[174]Arsigny, V., Commowick, O., Pennec, X., Ayache, N.. A log-Euclidean framework for statistics on diffeomorphisms [C]. In:Larsen, R., Nielsen, M., Sporting, J. (Eds.), Proc.MICCAI'06., LNCS, Springer Verlag,2006,4190:924-931.
[175]Tittgemeyer M, Wollny G, Kruggel F. Visualising deformation fields computed by non-linear image registration [J]. Computing and Visualization in Science,2002,5(1):45-51.
[176]Rohlfing T, Maurer Jr C R, Bluemke D A, Jacobs M A. Volume-preserving nonrigid registration of mr breast images using free-form deformation with an incompressibility constraint [J]. IEEE Transactions on Medical Imaging,2003,22(6):730-741.
[177]Marius George Linguraru, Tom Vercauteren, Mauricio Reyes-Aguirre, et al. Segmentation propagation from deformable atlases for brain mapping and analysis [J]. Brain Research Journal,2007,1(4): 1-18.
[178]Bruce Fischl, David H. Salat, Evelina Busa, et al. Whole brain segmentation:automated labeling of neuroanatomical structures in the human brain [J]. Neuron,2002,33(1):341-355.
[179]Yan Xia, Keith Bettinger, Lin Shen, et al. Automatic segmentation of the caudate nucleus from human brain MR Images [J]. IEEE Transactions on Medical Imaging,2007,26(4):509-517.
[180]P. Yushkevich, J. Piven, H. Hazlett, R. et al. User-guided 3-D active contour segmentation of anatomical structures:significantly improved efficiency and reliability [J]. Neurolmage,2006,31: 1116-1128.
[181]Pitiot, H. Delingette, P. Thompson, N. Ayache. Expert knowledge-guided segmentation system for brain MRI [J]. NeuroImage,2004,23:85-96.
[182]D. V. Iosifescu, M. E. Shenton, S. K.Warfield, et al. An automated registration algorithm for measuring MRI subcortical brain structures [J]. NeuroImage,1997,6:13-25.
[183]C. Baillard, P. Hellier, C. Barillot. Segmentation of brain 3D MR images using level sets and dense registration [J]. Medical Image Analysis,2001,5:185-194.
[184]V. Barra, J. Y. Boire. Automatic segmentation of subcortical brain structures in MR images using information fusion [J]. IEEE Trans. Med. Imag.,2001,20(7):549-558.
[185]Jing-Hao Xue, Su Ruan, Bruno Moretti, et al. Knowledge-based segmentation and labeling of brain structures from MRI images [J]. Pattern Recognition Letters,2001,22(3-4):395-405.
[186]Duta, N., Sonka, M.. Segmentation and interpretation of MR brain images:an improved active shape model [J]. IEEE Trans. Med. Imag.,1998,17(6):1049-1062.
[187]Cootes, T., Taylor, C.. Statistical models of appearance for medical image analysis and computer vision [J]. SPIE Medical Imaging,2001,4322:236-248.
[188]Ciofolo, C., Barillot, C.. Brain segmentation with competitive level sets and fuzzy control [C]. Information Processing in Medical Imaging,2005, LNCS 3565:333-344.
[189]Tsai, A.,Wells,W., Tempany, C.,et al. Mutual information in coupled multi-shape model formedical image segmentation [J]. Med. Image Anal,2004,8(4):429-445.
[190]Yang, J., Duncan, J..3d image segmentation of deformable objects with joint shape-intensity prior models using level sets [J]. Med. Image Anal.,2004,8:285-294.
[191]Yang, J., Staib, L., Duncan, J.. Neighbor-constrained segmentation with level set based 3-d deformable models [J]. IEEE Trans. Med. Imag.,2004,23(8):940-948.
[192]Tu, Z., Narr, K., Dollar, P., et al. Brain anatomical structure segmentation by hybrid discriminative/generative models [J]. IEEE Trans. Med. Imag.,2008,27(4):495-508.
[193]Jue Wu, Albert C.S. Chung. A novel framework for segmentation of deep brain structures based on Markov dependence tree [J]. NeuroImage,2009,46:1027-1036.
[194]Zhou, J., Rajapakse, J.. Segmentation of subcortical brain structures using fuzzy templates [J]. NeuroImage,2005,28:915-924.
[195]Cybele Ciofolo, Christian Barillot. Atlas-based segmentation of 3D cerebral structures with competitive level sets and fuzzy control [J]. Medical Image Analysis,2009,13:456-470.
[196]Pohl, K., Fisher, J., Grimson, W., et al. A bayesian model for joint segmentation and registration [J]. Neurolmage,2006,31:228-239.
[197]Pohl, K., Bouix, S., Nakamura, M., et al. A hierarchical algorithm for mr brain image parcellation [J]. IEEE Trans. Med. Imag.,2007,26:1201-1212.
[198]Pohl, K., Fisher, J., Bouix, S., et al. Using the logarithm of odds to define a vector space on probabilistic atlases [J]. Med. Image Anal.,2007,11(5):465-477.
[199]Gouttard, S., Styner, M., Joshi, S., et al. Subcortical structure segmentation using probabilistic atlas priors [C]. SPIE Medical Imaging,2007,6512:65111j-65122j.
[200]Khan, A.,Wang, L., Beg, M.. Freesurfer-initiated fully-automated subcortical brain segmentation in MRI using large deformation diffeomorphic metric mapping [J]. NeuroImage,2008,41:735-746.
[201]Heckemann, R., Hajnal, J., Aljabar, P., et al. Automatic anatomical brain mri segmentation combining label propagation and decision fusion [J]. NeuroImage,2006,33:115-126.
[202]Svarer, C.,Madsen, K., Hasselbalch, S., et al. MR-based automatic delineation of volumes of interest in human brain pet images using probability maps [J]. Neurolmage,2005,24:969-979.
[203]Gousias, I., Rueckert, D., Heckemann, R., et al. Automatic segmentation of brain MRIs of 2-year-olds into 83 regions of interest [J]. Neurolmage,2008,40:672-684.
[204]Akselrod-Ballin, A., Galun, M., Gomori, J.M., et al. Atlas guided identification of brain structures by combining 3D segmentation and SVM classification [C]. In Proceedings of the 9th International Conference on Medical Image Computing and Computer-Assisted Intervention,2006, LNCS 4191: 209-216.
[205]Powell, S., Magnotta, V.A., Johnson, H., et al. Registration and machine learning-based automated segmentation of subcortical and cerebellar brain structures [J]. NeuroImage,2008,39 (1):238-247.
[206]Akselrod-Ballin, A., Galun, M., Gomori, J.M., et al. Prior knowledge driven multiscale segmentation of brain MRI [C]. In Proceedings of the 10th International Conference on Medical Image Computing and Computer-Assisted Intervention,2007, LNCS 4792:118-126.
[207]Tohka, J., Wallius, E., Hirvonen, J., et al. Automatic extraction of caudate and putamen in [11C] raclopride PET using deformable surface models and normalized cuts [J]. IEEE Trans. Nucl. Sci.,2006,53 (1):220-227.
[208]J. Talairach, P. Tournoux. Coplanar stereotaxis atlas of the human brain [M]. Stuttgart:Mark Rayport, Trans. Thieme,1988.
[209]Meritxell Bach Cuadra. Atlas-based segmentation and classification of magnetic resonance brain images [D]. Ph.D. Thesis, Lausanne:EPFL,2003.
[210]T. Greitz, C. Bohm, S. Holte, L. Eriksson. A computerized brain atlas:construction, anatomical content and some applications [J]. Journal of Computer Assisted Tomography,1991,15(1):26-38.
[211]Bruce Fischl, David H. Salat, Andre J.W. van der Kouwe, et al. Sequence-independent segmentation of magnetic resonance images [J]. NeuroImage,2004,23(Supplement 1):S69-S84.
[212]X Han, B Fischl. Atlas Renormalization for Improved Brain MR Image Segmentation Across Scanner Platforms [J]. IEEE Transactions on Medical Imaging,2007,26(4):479-486.
[213]Peter Rogelj, Stanislav Kovacic. Symmetric image registration [J]. Medical Image Analysis,2006, 10(3):484-493.
[214]Xavier PENNEC, Pascal CACHIER, Nicholas AYACHE. Understanding the "Demon's algorithm": 3D non-rigid registration by gradient descent [C].2nd Int. Conf. on Medical Image Computing and Computer-Assisted Intervention (MICCAI'99), Cambridge, UK, Sept.19-22,1999, pp:597-605.
[215]A. Tikhonov, V. Arsenin. Solutions of ill-posed problems [M]. Winston and Salem, Washington, DC, 1977.
[216]H. Chui, A. Rangarajan. A new algorithm for non-rigid point matching [C]. IEEE Conf. on Computer Vision and Pattern Recognition,2000, pp. Ⅱ:44-51.
[217]S. Belongie, J. Malik, J. Puzicha. Matching Shapes [C]. IEEE Int'l Conf. on Computer Vision,2001, pp.456-461.
[218]R. H. Davies, C. J. Twining, T. F. Cootes, et al.3D statistical shape models using direct optimization of description length [C]. European Conf. on Computer Vision,2002, pp.3-20.
[219]L. H. Staib, J. S. Duncan. Boundary finding with parametrically deformable models [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1992,14(11):1061-1075.
[220]T. Sebastian, P. Klein, B. Kimia. Recognition of shapes by editting shock graphs [C]. IEEE Int'l Conf. on Computer Vision,2001, pp.755-762.
[221]N. Paragios, M. Rousson, V. Ramesh. Non-rigid registration using distance functions [J]. Computer Vision and Image Understanding,2003,89:142-165.
[222]M. Leventon, E. Grimson, O. Faugeras. Statistical shape influence in geodesic active contours [C]. IEEE Conf. on Computer Vision and Pattern Recognition,2000, pp.I:316-322.
[223]Xiaolei Huang, Nikos Paragios, Dimitris N. Metaxas. Shape registration in implicit spaces using information theory and free form deformations [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(8):1303-1317.
[224]D. Louis Collins, Georges Le Goualher, Alan C. Evans. Non-linear cerebral registration with sulcal constraints [C]. MICCAI,1998, pp:974-984.
[225]D. W. Shattuck, R. M. Leahy. BrainSuite:An automated cortical surface identification tool [J]. Med. Image Anal.,2002,6:129-142.
[226]S.M. Smith, M. Jenkinson, M.W. Woolrich, et al. Advances in functional and structural MR image analysis and implementation as FSL [J]. NeuroImage,2004,23(S1):208-219.
[227]A. Zijdenbos, B. Dawant, R. Margolin, A. Palmer, Morphometric analysis of white matter lesions in MR images:method and validation [J]. IEEE Transactions on Medical Imaging,1994,13 (4):716-724.
[2]Lisa Gottesfeld BROWN. A survey of image registration techniques[J]. ACM Computing Surveys, 1992,24(4):325-376.
[3]章毓晋.图像工程(第二版)[M].北京：清华大学出版社,2007.
[4]Lemieux L, Wieshmann UC Moran NF, Fish DR, Shorvon SD. The detection and significance of subtle changes in mixed-signal brain lesions by serial MRI scan matching and spatial normalization [J]. Medical Image Analysis,1998,2(3):227-242.
[5]Rao A, Chandrashekara R, Sanchez-Ortiz GI, et al. Spatial transformation of motion and deformation fields using nonrigid registration [J]. IEEE Transactions on Medical Imaging,2004,23 (9):1065-1076.
[6]Baete K, Nuyts J, Laere KV, et al. Evaluation of anatomy based reconstruction for partial volume correction in brain FDG-PET [J]. Neurolmage,2004,23(1):305-317.
[7]Lee J-M, Kim SH, Jang DP, et al. Deformable model with surface registration for hippocampal shape deformity analysis in schizophrenia [J]. NeuroImage,2004,22(2):831-840.
[8]'Richard FJP, Cohen LD. A new image registration technique with free boundary constraints: application to mammography[J]. Computer Vision and Image Understanding,2003,89(2-3):166-196.
[9]徐胜舟,宋恩民,许向阳.基于改进Demons算法的乳腺X线摄片非刚性配准[J].中国图象图形学报,2009,14(12)：2566-2571.
[10]杨健,王涌天,唐宋元,周寿军,刘越.基于互信息量和薄板样条的X射线造影图像弹性配准[J].电子学报.2007,35(1)：127-130.
[11]王君臣,王田苗,徐源,方礼明.基于ICP算法的手术导航三维配准技术[J].北京航空航天大学学报,2009,35(4)：434-438.
[12]葛云,章东.基于Legendre正交矩的配准方法在放疗导航中的应用研究[J].电子学报,2009,37(7)：1529-1531.
[13]陈芳,张建秋,胡波.基于块图像内容自适应选择的数字减影血管造影术的图像配准法[J].航天医学与医学工程,2009,22(1)：49-55.
[14]卢振泰.医学图像配准算法研究[D].博士学位论文,南方医科大学,2008.
[15]张少敏,支力佳,赵大哲,赵宏,杨金柱.基于区域联合Renyi熵的多模医学图像配准[J].电子学报,20·09,37(10)：2320-2325.
[16]刘晴,郭希娟,许慎洋.基于互信息的N维多模医学图像配准[J].中国图象图形学报,2009,14(10)：2061-2068.
[17]张加万,李谭,孙济洲.基于Dividing Rectangles的多模态医学图像配准算法[J].中国图象图形学报,2008,13(4)：749-755.
[18]何元烈,田联房,陈萍,王立非,叶广春,毛宗源.基于三次样条层间插值的多模态医学图像配准[J].生物医学工程学杂志,2007,24(6)：1241-1245.
[19]J. B. Antoine Maintz, Max A. Viergever. A survey of medical image registration [J]. Medical Image Analysis,1998,2(1):1-36.
[20]A. Ardeshir Goshtasby.2-D and 3-D image registration:for medical, remote sensing, and industrial applications[M]. A John Wiley & Sons, Inc., Publication,2005.
[21]Barbara Zitova, Jan Flusser. Image registration methods:a survey[J]. Image and Vision Computing, 2003,21:977-1000.
[22]Mei Chen.3-D deformable registration using a statistical atlas with applications in medicine [D]. Doctoral thesis, The Robotics Institute Carnegie Mellon University,1999.
[23]Ali Gholipour, Nasser Kehtarnavaz, Richard Briggs, et al. Brain functional localization:a survey of image registration techniques [J]. IEEE Transactions on Medical Imaging,2007,26 (4):427-451.
[24]Isaac H. Bankman. Handbook of medical image processing and analysis (second edition) [M]. Academic Press, USA,2009.
[25]刘新刚,陈武凡,陈光杰.基于先验知识和MRF随机场模型的医学图像弹性配准方法[J].中国生物医学工程学报.2006,25(2)：151-157.
[26]彭晓明,陈武凡,马茜.基于B样条的弹性点配准方法[J].中国图象图形学报,2007,12(6)：1079-1085.
[27]吴月娥,王小喜,哈章,李传富,周平,周康源.基于薄板样条和形状内容的医学图像非刚性配准方法研究[J].航天医学与医学工程,2007,20(1)：43-46.
[28]李静,杨烜,喻建平.基于特征分类的MRI医学图像弹性配准[J].中国图象图形学报,2007,12(6)：1069-1078.
[29]龚永义,罗笑南,贾维嘉,黄厚生.基于改进的弹簧质子模型的医学图像配准[J].计算机学报,2008,31(7)：1224-1233.
[30]廖秀秀,于慧敏,扬威.基于隐含形状表示和边缘信息融合的非刚体图像配准[J].中国生物医学工程学报,2008,27(3)：340-346.
[31]荣成城,周健,曹国刚,罗立民.基于粘滞流体B样条模型的快速非刚体配准方法[J].中国图象图形学报,2009,14(4)：712-717.
[32]J. L. Lancaster, T. G. Glass, B. R. Lankipalli, et al. A modality independent approach to spatial normalization of tomographic images of the human brain[J]. Hum. Brain Mapp.,1995,3:209-223.
[33]Y. Han and H. Park. Automatic registration of brain magnetic resonance images based on Talairach reference system [J]. J. Magn. Reson. Imag.,2004,20(10):572-580.
[34]K. Rohr, M. Fornefett, H. S. Stiehl. Spline-based elastic image registration:integration of landmark errors and orientation attributes [J]. Comput. Vis. Image Understanding,2003,90:153-168.
[35]K. Rohr, H. S. Stiehl, R. Sprengel, et al. Landmark-based elastic registration using approximating thin plate splines [J]. IEEE Trans. Med. Imag.,2001,20(6):526-534.
[36]M. Xu, W. L. Nowinski. Talairach-Toumoux brain atlas registration using a metal forming principle-based finite element method [J]. Med. Imag. Anal.,2001,5:271-279.
[37]Pielot R, Scholz M, Obermayer K, et al. A new point-based warping method for enhanced and simplified analysis of functional brain image data [J]. Neurolmage,2003,19(4):1716-1729.
[38]Xue Z, Shen D, Davatzikos C. Determining correspondence in 3-D MR brain images using attribute vectors as morphological signatures of voxels [J]. IEEE Transactions on Medical Imaging,2004,23(10): 1276-1291.
[39]Bookstein FL, Green WDK. A Feature space for edgels in images with landmarks [J]. J. Math. Imaging and Vision,1993,3:231-261.
[40]Mardia KV, Little JA. Image warping using derivative information [C]. Math. Methods in Med. Imaging Ⅲ, SPIE Vol.1994,2299:16-31.
[41]Joshi SC, Miller MI, Christensen GE, et al. Hierarchical brain mapping via a generalized dirichlet solution for mapping brain manifolds [C]. Vision Geometry Ⅳ, Proc. SPIE Conference on Optical Science, Engineering and Instrumentation, San Diego, CA, August.1995,2573:278-289.
[42]Ge Y, Fitzpatrick JM, Kessler RM, Jeske-Janicka M. Intersubject brain image registration using both cortical and subcortical landmarks [C]. SPIE Image Processing,1995,2434:81-95.
[43]P. Cachier, J.-F. Mangin, X. Pennec, et al. Multisubject non-rigid registration of brain MRI using intensity and geometric features [C]. Lecture Notes in Computer Science. Berlin, Germany: Springer-Verlag,2001,2208:734-742.
[44]E. Hellier, C. Barillot. Coupling dense and landmark-based approaches.for non-rigid registration [J]. IEEE Trans. Med. Imag.,2003,22(2):217-227.
[45]Declerck J, Subsol G, Thirion JP. Automatic retrieval of anatomical structures in 3D medical images [R]. INRIA Technical Report 2485,1995.
[46]Feldmar J, Declerck J, Malandain G, Ayache N. Extension of the ICP algorithm to nonrigid intensity-based registration of 3D volumes [J]. Computer Vision and Image Understanding,1997,66(2): 193-206.
[47]Cac Z, Pan S, Li R, et al. Registration of medical images using an interpolated closest point transform: method and validation [J]. Medical Image Analysis,2004,8(4):421-427.
[48]Liu Y. Improving ICP with easy implementation for free-form surface matching [J]. Pattern Recognition,2004,37(2):211-226.
[49]Thompson P, Toga AW. A surface-based technique for warping three-dimensional images of the brain [J]. IEEE Transactions on Medical Imaging,1996,15(4):402-417.
[50]D. Shen, C. Davatzikos. HAMMER:Hierarchical attribute matching mechanism for elastic registration [J]. IEEE Trans. Med. Imag.,2002,21(11):1421-1439.
[51]D. Shen. Image registration by hierarchical matching of local spatial intensity histograms [C]. Lecture Notes in Computer Science. Berlin, Germany:Springer-Verlag,2004,3216:582-590.
[52]G. Wu, F. Qi, D. Shen. Learning best features for deformable registration of MR brains [C]. Lecture Notes in Computer Science. Berlin, Germany:Springer-Verlag,2005,3750:179-187.
[53]G. Wu, F. Qi, D. Shen. Learning-based deformable registration of MR brain images [J]. IEEE Trans. Med. Imag.,2006,25(9):1145-1157.
[54]T. Liu, D. Shen, C. Davatzikos. Deformable registration of cortical structures via hybrid volumetric and surface warping [C]. Lecture Notes in Computer Science. Berlin, Germany:Springer-Verlag,2003, 2879:780-787.
[55]T. Liu, D. Shen, C. Davatzikos. Deformable registration of tumor-diseased brain images [C]. Lecture Notes in Computer Science. Berlin, Germany:Springer-Verlag,2004,3216:720-728.
[56]Z. Xue, D. Shen, C. Davatzikos. Correspondence detection using wavelet-based attribute vectors [C], Lecture Notes in Computer Science. Berlin, Germany:Springer-Verlag,2003,2879:762-770.
[57]Z. Xue, D. Shen, C. Davatzikos. Statistical representation of high-dimensional deformation fields with application to statistically constrained 3D warping [J]. Med. Image Anal.,2006,10:740-751.
[58]B. C. Vemuri, J. Ye, Y. Chen, C. M. Leonard. Image registration via level-set motion:applications to atlas-based segmentation [J]. Med. Image Anal.,2003,7:1-20.
[59]Y.Wang, L. H. Staib. Physical model-based non-rigid registration incorporating statistical shape information [J]. Med. Image Anal.,2000,4:7-20.
[60]Cohen LD, Cohen I. Deformable models for 3D medical images using finite elements & balloons [C]. 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Los Alamitos, CA, USA. IEEE Comput. Soc. Press,1992:592-598.
[61]A. Kassam, M. L. Wood. Fourier registration of three-dimensional brain MR images:exploiting the axis of rotation [J]. Journal of Magnetic Resonance Imaging,1996,6:894-902.
[62]Amit Y, Grenander U, Piccioni M. Structural image restoration through deformable templates [J]. J. Amer. Stat. Assoc,1991,86(414):376-387.
[63]Bookstein FL. Principal warps:thin-plate splines and the decomposition of deformations [J] IEEE Trans. Pattern Analysis and Machine Intelligence,1989,11(6):567-585.
[64]Bookstein FL. Proposal for a biometrics of the cortical surface:a statistical method for relative surface distance metrics [C]. Proc. of the SPIE Conference on Vision Geometry,1995,2573:312-323.
[65]Bookstein FL. Landmark methods for forms without landmarks:morphometrics of group differences in outline shape [J]. Medical Image Analysis,1997,1(3):225-243.
[66]Collins DL, Holmes CJ, Peters TM, Evans AC. Automatic 3D model-based neuroanatomical segmentation [J]. Human Brain Mapping,1995,3:190-208.
[67]S. Joshi, B. Davis, M. Jomier, G. Gerig. Unbiased diffeomorphic atlas construction for computational anatomy [J]. Neurolmage,2004,23:S151-S160.
[68]J. D. Gispert, J. Pascau, S. Reig, et al. Influence of the normalization template on the outcome of statistical parametric mapping of PET scans [J]. NeuroImage,2003,19:601-612.
[69]J. Ashburner, K. Friston. Multimodal image coregistration and partitioning-A unified framework [J]. NeuroImage,.1997,6:209-217.
[70]Thirion J P. Image matching as a diffusion process:an analogy with Maxwell's demons [J]. Medical Image Analysis,1998,2(3):243-260.
[71]J. Suckling, C. Long, C. Triantafyllou, et al. Variable precision registration via wavelets:optimal spatial scales for inter-subject registration of functional MRI [J]. Neuroimage,2006,31:197-208.
[72]R. Stefanescu, X. Pennec, N. Ayache. Grid powered nonlinear image registration with locally adaptive regularization [J]. Med. Image Anal.,2004,8:325-342..
[73]林相波,邱天爽,Frederic Nicolier,RUAN Su.Active Demons可变形图像配准算法研究[J].中国生物医学工程学报.2008,27(4)：636-640.
[74]J. Ashburner, J. L. R. Andersson, K. J. Friston. Image registration using a symmetric prior-in three dimensions [J]. Hum. Brain Mapp.,2000,9:212-225.
[75]A. Cuzol, P. Hellier, E. Memin. A novel parametric method for non-rigid image registration [C]. Lecture Notes in Computer Science. Berlin, Germany:Springer-Verlag,2005,3565:456-467.
[76]P. Hellier, J. Ashburner, I. Corouge, et al. InterSubject registration of functional and anatomical data using SPM [C]. Lecture Notes in Computer Science. Berlin, Germany:Springer-Verlag,2002,2489: 590-597.
[77]E.Hellier, C. Barillot, E. Memin, P. Perez. Hierarchical estimation of a dense deformation field for 3-D robust registration [J]. IEEE Trans. Med. Imag.,2001,20(5):388-402.
[78]G. E. Christensen, H. J. Johnson. Consistent image registration [J]. IEEE Trans. Med. Imag.,2001, 20(7):568-582.
[79]F. Crivello, T. Schormann, N. Tzourio-Mazoyer, et al. Comparison of spatial normalization procedures and their impact on functional maps [J]. Hum. Brain Mapp.,2002,16:228-250.
[80]Studholme C, Hill DLG, Hawkes DJ. Automated registration of truncated MR and CT datasets of the head [C]. Proc. Br. Mach. Vision Conf.,1995, pp:27-36.
[81]Collignon A, Maes F, Delaere D, et al. Automated multi-modality image registration based on information theory [M]. Information Processing in Medical Imaging, Bizais Y, Barillot C, Di Paola R, (eds.), pp.263-274, Dordrecht, The Netherlands:Kluwer Academic,1995.
[82]Maes F, Collignon A, VandermeulenD, et al. Multimodality image registration by maximization of mutual information [J]. IEEE Trans. Med. Imaging,1997,16:187-198.
[83]Wells Ⅲ WM, Viola P, Atsumi H, et al. Multi-modal volume registration by maximization of mutual information [J]. Med. Image Anal.,1996,1:35-51.
[84]D. Seghers, E. D'Agostino, F. Maes, et al. Construction of a brain template from MR images using state-of-the-art registration and segmentation techniques [C]. Lecture Notes in Computer Science. Berlin, Germany:Springer-Verlag,2004,3216:696-703.
[85]Q. Wang, D. Seghers, E. D'Agostino, et al. Construction and validation of mean shape atlas templates for atlas-based brain image segmentation [C]. Lecture Notes in Computer Science. Berlin, Germany: Springer-Verlag,2005,3565:689-700.
[86]M. De Craene, A. du Bois d'Aische, B. Macq, S. K. Warfield. Multi-subject variational registration for probabilistic unbiased atlas generation [C]. Proc. ICIP 2005:IEEE Int. Conf. Image Processing, Sep.2005, 3:111-601-Ⅲ-604.
[87]M.Jenkinson, S. Smith. A global optimisation method for robust affme registration of brain images [J]. Med. Imag. Anal.,2001,5:143-156.
[88]G. K. Rohde, A. Aldroubi, B. M. Dawant. The adaptive bases algorithm for intensity-based nonrigid image registration [J]. IEEE Trans. Med. Imag.,2003,22(11):1470-1479.
[89]E.D'Agostino, F. Maes, D.Vandermeulen, P. Suetens. Non-rigid atlas-to-image registration by minimization of class-conditional image entropy [C]. Lecture Notes in Computer Science. Berlin, Germany: Springer-Verlag,2004,3216:745-753.
[90]J. Ashburner, J. L. R. Andersson, K. J. Friston. High-dimensional image registration using symmetric priors [J]. NeuroImage,1999,9:619-628.
[91]衣文文,常发亮,胡彦磊.一种基于互信息的三级医学图像配准策略[J].航天医学与医学工程,2008,21(6)：518-522.
[92]张泾周,李婷,张佳.基于广义互信息的人脑图像配准研究[J].生物医学工程学杂志,2008,25(6)：1303-1306.
[93]K. K. Bhatia, J. V. Hajnal, B. K. Puri, et al. Consistent groupwise non-rigid registration for atlas construction [C]. Proc. IEEE Int. Symp. Biomedical Imaging:Macro to Nano, Apr.2004,1:908-911.
[94]D. Rueckert, A. F. Frangi, J. A. Schnabel. Automatic construction of 3-D statistical deformation models of the brain using nonrigid registration [J]. IEEE Trans. Med. Imag.,2003,22(8):1014-1025.
[95]C. Studholme, V. Cardenas, E. Song, et al. Accurate template-based correction of brain MRI intensity distortion with application to dementia and aging [J]. IEEE Trans. Med. Imag.,2004,23(1):99-110.
[96]P. Lorenzen, M. Prastawa, B. Davis, et al. Multi-modal image set registration and atlas formation [J]. Med. Imag. Anal.,2006,10:440-451.
[97]E.D'Agostino, F. Maes, D.Vandermeulen, P. Suetens. An information theoretic approach for non-rigid image registration using voxel class probabilities [C]. Lecture Notes in Computer Science. Berlin, Germany: Springer-Verlag,2003,2879:812-820.
[98]P. J. Lorenzen, S. C. Joshi. High-dimensional multi-modal image registration [C]. Lecture Notes in Computer Science. Berlin, Germany:Springer-Verlag,2003,2717:234-243.
[99]R. P. Woods, S. R. Cherry, J. C. Mazziotta. Rapid automated algorithm for aligning and reslicing PET images [J]. J. Comput. Assist. Tomogr.,1992,16:620-633.
[100]R. P. Woods, S. T. Grafton, C. J. Holmes, et al. Automated image registration:I. General methods and intrasubject, intramodality validation [J]. J. Comput. Assist. Tomogr,1998,22:139-152.
[101]R. P. Woods, J. C. Mazziotta, S. R. Cherry. MRI-PET registration with automated algorithm [J]. J. Comput. Assist. Tomogr.,1993,17:536-546.
[102]H. Lester, S. R. Arridge. A survey of hierarchical non-linear medical image registration [J]. Pattern Recognition,1999,32(1):129-149.
[103]C. Broit. Optimal registration of deformed images [D]. Ph.D. dissertation, Dept. Comput. Inf. Sci., Univ. Pennsylvania, Philadelphia,1981.
[104]R. Bajcsy, R. Lieberson, M. Reivich. A computerized system for the elastic matching of deformed radiographic images to idealized atlas images [J]. J. Comput. Assist. Tomogr.,1983,7(4):618-625,.
[105]J. C. Gee, D. R. Haynor, M. Reivich, R. Bajcsy. Finite element approach to warping of brain images [C]. SPIE Med. Imag.,1994,2167:327-337.
[106]R. Bajcsy, S. Kovacic. Multiresolution elastic matching [J]. Comput. Vision Graph. Image Process., 1989,46(1):1-21.
[107]C. Davatzikos, R. N. Bryan. Using a deformable surface model to obtain a shape representation of the cortex [J]. IEEE Trans. Med. Imag.,1996,15(6):785-795.
[108]C. Davatzikos. Spatial transformation and registration of brain images using elastically deformable models [J]. Comput. Vision Image Understand.,1997,66:207-222.
[109]G. E. Christensen, R. D. Rabbitt, M. I. Miller.3D brain mapping using a deformable neuroanatomy [J]. Phys. Medicine Biol.,1994,39:609-618.
[110]G. E. Christensen, R. D, Rabbitt, M. I. Miller. Deformable templates using large deformation kinematics [J]. IEEE Trans. Image Process.,1996,5:1435-1447.
[111]G. E. Christensen, S. C. Joshi, M. I. Miller. Volumetric transformation of brain anatomy [J]. IEEE Trans. Med. Imag.,1997,16(6):864-877.
[112]M. Bro-Nielsen, C. Gramkow. Fast fluid registration of medical images [C]. Proc. Visualization in Biomedical Computing, R. Kikinis and K. Hoehne, Eds. Berlin, Germany:Springer Verlag,1996, pp. 267-276.
[113]Thirion, J.P.. Fast non-rigid matching of 3D medical images [R], Technical report 2547, INRIA, 1995.
[114]Meritxell Bach Cuadra. Atlas-based segmentation and classification of magnetic resonance brain images [D]. Ph.D thesis, Ecole Polytechnique Federale De Lausanne, Switzerland,2003.
[115]P. Cachier, X. Pennec, N. Ayache. Fast non rigid matching by gradient descent:study and improvements of the "Demons" algorithm [R]. Technical Report, INRIA 3706,1999.
[116]He Wang, Lei Dong, J O'Daniel, et al. Validation of an accelerated'demons'algorithm for deformable image registration in radiation therapy [J]. Phys. Med. Biol.,2005,50(12):2887-2905.
[117]Xavier Pennec, Pascal Cachier, Nicholas Ayache. Understanding the demon's algorithm:3D non-rigid registration by gradient descent [C]. Proceedings of the 2nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI'99), Lecture Notes in Computer Science, Springer-Verlag,1999,1679:597-605.
[118]Pascal Cachier, Eric Bardinet, Didier Dormont, et al. Iconic feature based nonrigid registration:The PASHA algorithm [J]. Computer vision and image understanding,2003,89(2-3):272-298.
[119]Tom Vercauteren, Xavier Pennec, Aymeric Perchant, Nicholas Ayache. Non-parametric diffeomorphic image registration with the demons algorithm [C]. Proceedings of the 10th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI'07), Brisbane, Australia, October 2007, Lecture Notes in Computer Science, Springer-Verlag,4792:319-326.
[120]XiiangBo Lin, Su Ruan, Frederic Morain-Nicolier, TianShuang Qiu. Non-rigid registration based segmentation of brain subcortical structures using a priori knowledge [C]. The proceedings of the 30th Annual International IEEE EMBS Conference. Vancouver, British Columbia, Canada, August 20-24,2008, pp:3971-3974.
[121]Xiangbo LIN, Tianshuang QIU, Frederic Nicolier, Su RUAN. An improved method of "Demons" non-rigid image registration algorithm [C]. The proceedings of the 9th International Conference on Signal Processing, Beijing China, October 26-29,2008, pp:1091-1094.
[122]A. Guimond, A. Roche, N. Ayache, J. Meunier. Three-dimensional multimodal brain warping using the demons algorithm and adaptive intensity corrections[J]. IEEE Trans. Med. Imag.,2001,20(1):58-69.
[123]J. D. Humphrey. Continuum biomechanics of soft biological tissues [C]. Proc. R. Stat. Soc.,2003, 459(2029):3-46.
[124]Mark Holden. A review of geometric transformation for nonrigid body registration [J]. IEEE Trans. Med. Imag.,2008,27(1):111-128.
[125]R. P. Woods, S. T Grafton, J. D. G. Watson, et al. Automated image registration:Ⅱ. intersubject validation of linear and nonlinear models [J]. Journal of Computer Assisted Tomography,1998, 22(1):153-165.
[126]R Szeliski, S. Lavall'ee. Matching 3-D anatomical surfaces with non-rigid deformations using octree-splines [C]. IEEE Workshop on Biomedical Image Analysis,1994, pp:144-153.
[127]J. Ashburner, K. J. Friston. Nonlinear spatial normalization using basis functions [J]. Human Brain Mapping,1999,7:254-266.
[128]Y. Amit. A nonlinear variational problem for image matching [J]. SIAM Journal on Scientific Computing,1994,15(1):207-224.
[129]S. Gefen, E. Tretiak, J. Nissanov. Elastic 3D alignment of rat brain histological images [J]. IEEE Trans. Med. Imag.,2003,22(11):1480-1489.
[130]Y. T. Wu, L. F. Chen, P. L. Lee, et al. Discrete signal matching using coarse-to-fine wavelet basis functions [J]. Pattern Recognition,2003,36(1):171-192.
[131]M. Fornefett, K. Rohr, H. S. Steihl. Radial basis functions with compact support for elastic registration of medical images [J]. Image Vis:Comput,2001,19(1-2):87-96.
[132]I. Barrodale, D. Skea, M. Berkley, et al. Warping digital images using thin plate splines [J]. Pattern Recognition,1993,26:375-376.
[133]J. Kohlrausch, K. Rohr, H. S. Stiehl. A new class of elastic body splines for nonrigid registration of medical images [J]. J. Math. Imag. Vis.,2005,23(3):253-280.
[134]S. W. K. Rohr. Physics-based elastic image registration using splines and including landmark localization uncertainties [C]. Med. Imag., Comput. Computer-Assisted Intervention-MICCAF 06,2006, (2):678-685.
[135]D. Rueckert, L. I. Sonoda, C. Hayes, et al. Nonrigid registration using free-form deformations: application to breast MR images [J]. IEEE Trans. Med. Imag.,1999,18(8):712-721.
[136]J. M. Declerck, J. Feldmar, M. L. Goris, F. Betting. Automatic registration and alignment on a template of cardiac stress and rest reoriented SPECT images [J]. IEEE Trans. Med. Imag.,1997,16(6): 727-737.
[137]Rueckert, D., Aljabar, P., Heckemann, et al. Diffeomorphic registration using B-splines [C].9th International Conference on Medical Image Computing and Computer-Assisted Intervention:MICCAI, 2006,4191:702-709.
[138]P. Thevanaz, T. Blu, M. Unser. Interpolation revisited [J]. IEEE Trans. Med. Imag.,2000,19(7): 739-758.
[139]M. W. Hsu, J. F. Hughes, H. Kaufman. Direct manipulation of free-form deformations [C]. Proc. SIGGRAPH,1992,6:177-184.
[140]J. Kybic, M. Unser. Fast parametric elastic image registration [J]. IEEE Trans. Image Process.,2003, 12(11):1427-1442.
[141]E.D'Agostino, F. Maes, D.. An information theoretic approach for non-rigid image registration using voxel class probabilities [J]. Med. Image Anal.,2006,10:413-431.
[142]S. Ourselin, A. Roche, S. Prima,]N. Ayache. Block matching:a general framework to improve robustness of rigid registration of medical images [C]. Lecture Notes in Computer Science. Berlin, Germany:Springer-Verlag,2000,1935:557-566.
[143]V. A. Magnotta, H. J. Bockholt, H. J. Johnson, et al. Subcortical, cerebellar, and magnetic resonance based consistent brain image registration [J]. Neurolmage,2003,19:233-245.
[144]Arno Klein, Jesper Andersson, Babak A. Ardekani, et al. Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration [J]. Neurolmage,2009,46:786-802.
[145]A. Tikhonov, V. Arsenin. Solutions of ill-posed problems [M]. Winston and Salem, Washington, DC, 1977.
[146]Meritxell Bach Cuadra. Atlas-based segmentation and classification of magnetic resonance brain images [D]. Ph.D thesis,2003, Ecole Polytechnique Federale De Lausanne, Switzerland.
[147]L. G. Nyul, J. K. Udupa, X. Zhang. New variants of a method of MRI scale standardization [J]. IEEE Trans. Med. Imag.,2000,19(2):143-150.
[148]Y. Ge, J. K. Udupa, L. G. Nyul, et al. Numerical tissue characterization in MS via standardization of the MR image intensity scale [J]. J. Magn. Reson. Imag.,2000,12(5):715-721.
[149]P. Hellier. Consistent intensity correction of MRimages [C]. Int. Conf. Image Process. (ICIP 2003), Sept.2003,1:1109-1112.
[150]V. Noblet, C. Heinrich, F. Heitz, et al. Retrospective evaluation of a topology preserving non-rigid registration method [J]. Medical Image Analysis,2006,10:366-384.
[151]Senthil Periaswamy, Hany Farid, Elastic registration in the presence of intensity variations [J]. IEEE Transactions on Medical Imaging,2003,22 (7):865-874.
[152]B.K.P. Horn. Robot Vision [M]. MIT press, Cambridge, MA,1986.
[153]Ibanez, L., Schroeder, W., Ng, L., Cates, J.. The ITK software guide:the insight segmentation and registration tool [R],2005. Kitware Inc. (http://www.itk.org).
[154]Daniel Schwarz, Tomas Kasparek, Ivo Provaznik, Jiri Jarkovsky. A deformable registration method for automated morphometry of MRI brain images in neuropsychiatric research [J]. IEEE Transactions on Medical Imaging,2007,26(4):452-461.
[155]R. Kikinis, M.E. Shenton, D.V. Iosifescu, et al. A digital brain atlas for surgical planning, model driven segmentation and teaching [J], IEEE Trans. Visual. Comput. Graph.,1996,2 (3):232-241.
[156]Internet Brain Segmentation Repository [DB]. M. G. H. Tech. Rep., Ctr. Morphometric Anal. IBSR, 2000 [Online].(http://neurowww.mgh.harvard.edu/cma/ibsr)
[157]John Ashburner. A fast diffeomorphic image registration algorithm [J]. Neurolmage,2007,38: 95-113.
[158]B.B. Avants, C.L. Epstein, M. Grossman, J.C. Gee. Symmetric diffeomorphic image registration with cross-correlation:evaluating automated labeling of elderly and neurodegenerative brain [J]. Medical Image Analysis,2008,12:26-41.
[159]Deshan Yang, Hua Li, Daniel A Low, et al. A fast inverse consistent deformable image registration method based on symmetric optical flow computation [J]. Phys. Med. Biol.,2008,53:6143-6165.
[160]Alex D. Leow, Igor Yanovsky, Ming-Chang Chiang, et al. Statistical properties of Jacobian maps and the realization of unbiased large-deformation nonlinear image registration [J]. IEEE Transactions On Medical Imaging,2007,26(6):822-832.
[161]Tom Vercauteren, Xavier Pennec, Aymeric Perchant, Nicholas Ayache. Diffeomorphic demons: efficient non-parametric image registration [J]. NeuroImage,2009,45:S61-S72.
[162]Alvarez L, Deriche R, Papadopoulo T, Javier Sanchez. Symmetrical dense optical flow estimation with occlusions detection [J]. Int. J. Comput. Vis.,2007,75:371-385.
[163]Leow A, Huang S-C, Geng A, et al. Inverse consistent mapping in 3D deformable image registration: its construction and statistical properties [J]. Information Processing in Medical Imaging (Berlin:Springer). 2005,3565:493-503.
[164]Dupuis P, Grenander U, Miller M I. Variational problems on flows of diffeomorphisms for image matching [J]. Quarterly of Applied Mathematics,1998,56:587-600.
[165]Christensen, G.E., Rabbitt, R.D., Miller, M.I..3D brain mapping using a deformable neuroanatomy [J]. Phys. Med. Biol.,1994,39:609-618.
[166]M. I. Miller, A. Trouve, L. Younes. On the metrics and Euler-Lagrange equations of computational anatomy [J]. Annu. Rev. Biomed. Eng.,2002,4:375-405.
[167]Beg, M.F., Miller, M.I., Trouve, A., Younes, L.. Computing large deformation metric mappings via geodesic flows of diffeomorphisms [J]. Int J. Comput. Vis.,2005,61(2):139-157.
[168]Avants B, Gee J. Symmetric geodesic shape averaging and shape interpolation [J]. Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis,2004,3117:99-110.
[169]Beg M F, Khan A. Symmetric data attachment terms for large deformation image registration [J]. IEEE Trans. Med. Imaging,2007,26:1179-1189.
[170]Musse O, Heitz F, Armspach J P. Topology preserving deformable image matching using constrained hierarchical parametric models [J]. IEEE Transactions on Image Processing,2001,10(7):1080-1093.
[171]Karacah B, Davatzikos C. Estimating topology preserving and smooth displacement fields [J]. IEEE Transactions on Medical Imaging,2004,23(7):868-880.
[172]Noblet V, Heinrich C, Heitz F, Armspach J P.3-D deformable image registration:A topology preservation scheme based on hierarchical deformation models and interval analysis optimization [J]. IEEE Transactions on Image Processing,2005,14(5):553-566.
[173]Michael Sdika. A Fast Nonrigid Image Registration With Constraints on the Jacobian Using Large Scale Constrained Optimization [J]. IEEE Transactions On Medical Imaging,2008,27(2):271-281.
[174]Arsigny, V., Commowick, O., Pennec, X., Ayache, N.. A log-Euclidean framework for statistics on diffeomorphisms [C]. In:Larsen, R., Nielsen, M., Sporting, J. (Eds.), Proc.MICCAI'06., LNCS, Springer Verlag,2006,4190:924-931.
[175]Tittgemeyer M, Wollny G, Kruggel F. Visualising deformation fields computed by non-linear image registration [J]. Computing and Visualization in Science,2002,5(1):45-51.
[176]Rohlfing T, Maurer Jr C R, Bluemke D A, Jacobs M A. Volume-preserving nonrigid registration of mr breast images using free-form deformation with an incompressibility constraint [J]. IEEE Transactions on Medical Imaging,2003,22(6):730-741.
[177]Marius George Linguraru, Tom Vercauteren, Mauricio Reyes-Aguirre, et al. Segmentation propagation from deformable atlases for brain mapping and analysis [J]. Brain Research Journal,2007,1(4): 1-18.
[178]Bruce Fischl, David H. Salat, Evelina Busa, et al. Whole brain segmentation:automated labeling of neuroanatomical structures in the human brain [J]. Neuron,2002,33(1):341-355.
[179]Yan Xia, Keith Bettinger, Lin Shen, et al. Automatic segmentation of the caudate nucleus from human brain MR Images [J]. IEEE Transactions on Medical Imaging,2007,26(4):509-517.
[180]P. Yushkevich, J. Piven, H. Hazlett, R. et al. User-guided 3-D active contour segmentation of anatomical structures:significantly improved efficiency and reliability [J]. Neurolmage,2006,31: 1116-1128.
[181]Pitiot, H. Delingette, P. Thompson, N. Ayache. Expert knowledge-guided segmentation system for brain MRI [J]. NeuroImage,2004,23:85-96.
[182]D. V. Iosifescu, M. E. Shenton, S. K.Warfield, et al. An automated registration algorithm for measuring MRI subcortical brain structures [J]. NeuroImage,1997,6:13-25.
[183]C. Baillard, P. Hellier, C. Barillot. Segmentation of brain 3D MR images using level sets and dense registration [J]. Medical Image Analysis,2001,5:185-194.
[184]V. Barra, J. Y. Boire. Automatic segmentation of subcortical brain structures in MR images using information fusion [J]. IEEE Trans. Med. Imag.,2001,20(7):549-558.
[185]Jing-Hao Xue, Su Ruan, Bruno Moretti, et al. Knowledge-based segmentation and labeling of brain structures from MRI images [J]. Pattern Recognition Letters,2001,22(3-4):395-405.
[186]Duta, N., Sonka, M.. Segmentation and interpretation of MR brain images:an improved active shape model [J]. IEEE Trans. Med. Imag.,1998,17(6):1049-1062.
[187]Cootes, T., Taylor, C.. Statistical models of appearance for medical image analysis and computer vision [J]. SPIE Medical Imaging,2001,4322:236-248.
[188]Ciofolo, C., Barillot, C.. Brain segmentation with competitive level sets and fuzzy control [C]. Information Processing in Medical Imaging,2005, LNCS 3565:333-344.
[189]Tsai, A.,Wells,W., Tempany, C.,et al. Mutual information in coupled multi-shape model formedical image segmentation [J]. Med. Image Anal,2004,8(4):429-445.
[190]Yang, J., Duncan, J..3d image segmentation of deformable objects with joint shape-intensity prior models using level sets [J]. Med. Image Anal.,2004,8:285-294.
[191]Yang, J., Staib, L., Duncan, J.. Neighbor-constrained segmentation with level set based 3-d deformable models [J]. IEEE Trans. Med. Imag.,2004,23(8):940-948.
[192]Tu, Z., Narr, K., Dollar, P., et al. Brain anatomical structure segmentation by hybrid discriminative/generative models [J]. IEEE Trans. Med. Imag.,2008,27(4):495-508.
[193]Jue Wu, Albert C.S. Chung. A novel framework for segmentation of deep brain structures based on Markov dependence tree [J]. NeuroImage,2009,46:1027-1036.
[194]Zhou, J., Rajapakse, J.. Segmentation of subcortical brain structures using fuzzy templates [J]. NeuroImage,2005,28:915-924.
[195]Cybele Ciofolo, Christian Barillot. Atlas-based segmentation of 3D cerebral structures with competitive level sets and fuzzy control [J]. Medical Image Analysis,2009,13:456-470.
[196]Pohl, K., Fisher, J., Grimson, W., et al. A bayesian model for joint segmentation and registration [J]. Neurolmage,2006,31:228-239.
[197]Pohl, K., Bouix, S., Nakamura, M., et al. A hierarchical algorithm for mr brain image parcellation [J]. IEEE Trans. Med. Imag.,2007,26:1201-1212.
[198]Pohl, K., Fisher, J., Bouix, S., et al. Using the logarithm of odds to define a vector space on probabilistic atlases [J]. Med. Image Anal.,2007,11(5):465-477.
[199]Gouttard, S., Styner, M., Joshi, S., et al. Subcortical structure segmentation using probabilistic atlas priors [C]. SPIE Medical Imaging,2007,6512:65111j-65122j.
[200]Khan, A.,Wang, L., Beg, M.. Freesurfer-initiated fully-automated subcortical brain segmentation in MRI using large deformation diffeomorphic metric mapping [J]. NeuroImage,2008,41:735-746.
[201]Heckemann, R., Hajnal, J., Aljabar, P., et al. Automatic anatomical brain mri segmentation combining label propagation and decision fusion [J]. NeuroImage,2006,33:115-126.
[202]Svarer, C.,Madsen, K., Hasselbalch, S., et al. MR-based automatic delineation of volumes of interest in human brain pet images using probability maps [J]. Neurolmage,2005,24:969-979.
[203]Gousias, I., Rueckert, D., Heckemann, R., et al. Automatic segmentation of brain MRIs of 2-year-olds into 83 regions of interest [J]. Neurolmage,2008,40:672-684.
[204]Akselrod-Ballin, A., Galun, M., Gomori, J.M., et al. Atlas guided identification of brain structures by combining 3D segmentation and SVM classification [C]. In Proceedings of the 9th International Conference on Medical Image Computing and Computer-Assisted Intervention,2006, LNCS 4191: 209-216.
[205]Powell, S., Magnotta, V.A., Johnson, H., et al. Registration and machine learning-based automated segmentation of subcortical and cerebellar brain structures [J]. NeuroImage,2008,39 (1):238-247.
[206]Akselrod-Ballin, A., Galun, M., Gomori, J.M., et al. Prior knowledge driven multiscale segmentation of brain MRI [C]. In Proceedings of the 10th International Conference on Medical Image Computing and Computer-Assisted Intervention,2007, LNCS 4792:118-126.
[207]Tohka, J., Wallius, E., Hirvonen, J., et al. Automatic extraction of caudate and putamen in [11C] raclopride PET using deformable surface models and normalized cuts [J]. IEEE Trans. Nucl. Sci.,2006,53 (1):220-227.
[208]J. Talairach, P. Tournoux. Coplanar stereotaxis atlas of the human brain [M]. Stuttgart:Mark Rayport, Trans. Thieme,1988.
[209]Meritxell Bach Cuadra. Atlas-based segmentation and classification of magnetic resonance brain images [D]. Ph.D. Thesis, Lausanne:EPFL,2003.
[210]T. Greitz, C. Bohm, S. Holte, L. Eriksson. A computerized brain atlas:construction, anatomical content and some applications [J]. Journal of Computer Assisted Tomography,1991,15(1):26-38.
[211]Bruce Fischl, David H. Salat, Andre J.W. van der Kouwe, et al. Sequence-independent segmentation of magnetic resonance images [J]. NeuroImage,2004,23(Supplement 1):S69-S84.
[212]X Han, B Fischl. Atlas Renormalization for Improved Brain MR Image Segmentation Across Scanner Platforms [J]. IEEE Transactions on Medical Imaging,2007,26(4):479-486.
[213]Peter Rogelj, Stanislav Kovacic. Symmetric image registration [J]. Medical Image Analysis,2006, 10(3):484-493.
[214]Xavier PENNEC, Pascal CACHIER, Nicholas AYACHE. Understanding the "Demon's algorithm": 3D non-rigid registration by gradient descent [C].2nd Int. Conf. on Medical Image Computing and Computer-Assisted Intervention (MICCAI'99), Cambridge, UK, Sept.19-22,1999, pp:597-605.
[215]A. Tikhonov, V. Arsenin. Solutions of ill-posed problems [M]. Winston and Salem, Washington, DC, 1977.
[216]H. Chui, A. Rangarajan. A new algorithm for non-rigid point matching [C]. IEEE Conf. on Computer Vision and Pattern Recognition,2000, pp. Ⅱ:44-51.
[217]S. Belongie, J. Malik, J. Puzicha. Matching Shapes [C]. IEEE Int'l Conf. on Computer Vision,2001, pp.456-461.
[218]R. H. Davies, C. J. Twining, T. F. Cootes, et al.3D statistical shape models using direct optimization of description length [C]. European Conf. on Computer Vision,2002, pp.3-20.
[219]L. H. Staib, J. S. Duncan. Boundary finding with parametrically deformable models [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1992,14(11):1061-1075.
[220]T. Sebastian, P. Klein, B. Kimia. Recognition of shapes by editting shock graphs [C]. IEEE Int'l Conf. on Computer Vision,2001, pp.755-762.
[221]N. Paragios, M. Rousson, V. Ramesh. Non-rigid registration using distance functions [J]. Computer Vision and Image Understanding,2003,89:142-165.
[222]M. Leventon, E. Grimson, O. Faugeras. Statistical shape influence in geodesic active contours [C]. IEEE Conf. on Computer Vision and Pattern Recognition,2000, pp.I:316-322.
[223]Xiaolei Huang, Nikos Paragios, Dimitris N. Metaxas. Shape registration in implicit spaces using information theory and free form deformations [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(8):1303-1317.
[224]D. Louis Collins, Georges Le Goualher, Alan C. Evans. Non-linear cerebral registration with sulcal constraints [C]. MICCAI,1998, pp:974-984.
[225]D. W. Shattuck, R. M. Leahy. BrainSuite:An automated cortical surface identification tool [J]. Med. Image Anal.,2002,6:129-142.
[226]S.M. Smith, M. Jenkinson, M.W. Woolrich, et al. Advances in functional and structural MR image analysis and implementation as FSL [J]. NeuroImage,2004,23(S1):208-219.
[227]A. Zijdenbos, B. Dawant, R. Margolin, A. Palmer, Morphometric analysis of white matter lesions in MR images:method and validation [J]. IEEE Transactions on Medical Imaging,1994,13 (4):716-724.