冷冻电镜生物大分子三维重构关键技术研究
|
摘要
现代计算机技术为结构生物学在原子和亚细胞空间尺度上结构信息的表述、存储、获取和分析作出了巨大贡献。结构生物学是生命科学的重要学科之一。生物大分子三维结构作为其核心内容,是研究生物大分子结构及其功能相互关系的前提和基础,测定生物大分子的高分辨率结构对于了解其相应的功能具有重要意义。冷冻电镜图像生物大分子三维重构技术是获得生物大分子结构的主要方法之一。
在冷冻电镜图像三维重构过程中,面临三大关键问题:(1)冷冻电镜生物大分子图像分割问题。冷冻电镜图像分割对于冷冻电镜图像高层次的特征提取、目标识别和图像配准等至关重要。但由于其低对比度和低信噪比等特点,很难达到理想的分割效果。(2)冷冻电镜图像生物大分子颗粒识别问题。生物大分子颗粒识别是冷冻电镜单粒子三维重构方法非常重要的环节,直接影响三维重构效率和三维结构精度。从低对比度和低信噪比的冷冻电镜图像中提取成千上万个生物大分子颗粒是一件相当耗时的工作,已经成为三维重构过程的瓶颈,自动颗粒识别方法的研究已成为当前关注的焦点。(3)电子断层扫描旋转图像配准问题。在电子断层扫描图像拍摄过程中,由于样本转动轴在旋转时的机械误差导致了旋转过程中样本的移动,直接影响三维结构的精度。因此,电子断层扫描旋转图像配准是电子断层扫描三维重构的关键环节。
本论文围绕如何利用现代计算机技术以及数字图像处理技术提高冷冻电镜图像三维重构的效率和三维的结构精度开展了深入分析和系统研究,取得了如下的研究成果:
1.针对冷冻电镜生物大分子图像的低信噪比和低对比度等特点,提出了基于小波变换和高斯差分的冷冻电镜生物大分子图像分割算法。该算法运用小波变换对冷冻电镜图像进行多尺度分解后,对低分辨率图像计算高斯差分图像,并对其进行基于灰度梯度的阈值分割,最后基于梯度信息进一步将低分辨率图像分割结果逐层过渡到高分辨率原图像上。该算法可以有效地抑制噪声,区分不同尺度的边缘信号,与canny算法以及最大类间方差方法的实验结果对比,该算法在主观评价和客观定量分析上均表现出更好的分割效果。
2.本文针对冷冻电镜图像颗粒区域难以提取的问题,提出了基于高斯差分掩模的形状特征提取算法。该算法通过计算颗粒样本的高斯差分图像掩膜,提取目标对象的近似区域进行形状特征提取。通过实验与基于方差图像的方法相比,该算法所提取的形状特征的单一识别率更高,具有更好的鉴别能力和分类性能。
3.针对冷冻电镜图像质量差、颗粒形状复杂多样的特点,提出了基于偏最小二乘的冷冻电镜图像颗粒识别二步方法。该二步方法包括基于高斯函数的候选颗粒检测和基于偏最小二乘的候选颗粒识别两个步骤。该方法运用基于PLS的识别机制,具有小样本学习特点,只需少量样本,就能保证在保持良好的正选率情况下,错选率很低,实验结果表明,该方法对方形颗粒的错选率为14%,对圆形颗粒的错选率只有7%。其次,采用了多种类型特征集合,可以对不同颗粒形状进行识别。实验结果验证了该方法具有通用性强,识别率高等特点。与同类算法相比,该方法在错选率方面具有优势。
4.针对电子断层扫描图像受噪声污染严重的特点,提出了基于局部互相关的电子断层扫描旋转图像配准优化模型。该模型克服了传统全局互相关方法固有的对噪声敏感的局限性,使配准结果更精确。该模型还具有良好的可扩展性,即可在此模型基础上,灵活定义相似度评价函数而无需改变整个算法框架。实验结果显示,该方法比同类基于全局互相关的方法表现出更好的配准精确度。
5.为了获得优化模型的最优解,需要对整个变换系数空间进行搜索,使算法效率非常低。根据图像序列前后图像偏移的相关性,提出了电子断层扫描旋转图像配准优化模型的自适应搜索机制。该机制能缩小变换方程系数空间的搜索范围,使配准过程能快速有效的进行。实验数据显示,使用该搜索机制,算法效率至少可以提高70%。
Mordern computer technology has made a significant contribution to the study of structural biology on expressing, storing, acquiring and analyzing structural information. Structural biology is one of the most important subjects of life sciences. Its key research content, the three-dimensional (3D) structure of biological macromolecules, is the premise and foundation of studying the relationship between macromolecule structure and function. Determining high resolution 3D structure of macromolecule is significant to understanding its corresponding function. Electron cryomicroscopy (Cryo-EM) is one of the main methods to obtain the macromolecules structure.
There are three key issues which is crucial to Cryo-EM 3D reconstruction. The first problem is segmentation of Cryo-EM image, which is crucial to the high-level processing of electron micrograph, including feature extraction, particle recognition, image registration and so on. But Cryo-EM image has the characteristic of low contrast and low signal-to-noise ratio (SNR) and is difficult to acquire ideal segmentation effect. The second is macromolecule particle recognition from Cryo-EM image, which is a crucial step of single particle reconstruction and has directly effect on the efficiency of Cryo-EM 3D reconstruction and the resolution of 3D structure. Selecting hundreds of thousands of particles from low contrast and low SNR Cryo-EM image is one of the major bottle-necks in advancing toward achieving atomic resolution reconstruction of biological macromolecules. The third problem is alignment of transmission electron microscope tilting series. Mechanical inaccuracies of the specimen holder cause specimen movement during tilting, which requires appropriate compensating adjustment. Accurate image alignment is needed for computing three-dimensional reconstructions from transmission electron microscope (TEM) tilt series.
This paper has made deeply research on how to use modern computer technology and image processing technology to enhance the efficiency of CryoEM three-dimensional reconstruction and the resolution of 3D structure. Several finding has been made, which is as follows.
1. This paper provides a new Cryo-EM image segmentation method based on wavelet transform and difference of Gaussian (DoG), which performs multi-scale decomposition based on wavelet transform at first. And then this method gets the DoG image of low-resolution image and does threshing segmentation based on the combination of gray and gradient information. At last, the segmentation result in low-resolution image is transferred to the high-resolution image based on the gradient. This method can overcome the flaw of the low SNR, and experiments show that it can get better segmentation effect than the canny algorithm and OTSU method.
2. It is difficult to get particle region in Cryo-EM images, which leads to the difficulty of shape fearture extraction. This paper proposed a shape feature extraction algorithm based on DoG mask. This algorithm only obtains the approximate region of objects, from which the shape features are extracted. Experiments show the shape feature extracted by this method has better ability of classification than the method based on variance image.
3. As to the problem of particle recognition, a two step approach for the particle recognition from Cryo-EM images has been developed in this paper, which is based on the principle of partial least square (PLS). This approach involves two steps: the detection of candidate particles based on Gaussian function and the recognition from these candidates based on PLS. This method is characterized by small sample study and can obtain satisfied recognition result with a few samples needed. Experiments show it can get low error-select-rate, which is 14% while selecting rectangular particle and only 7% while selecting circle particle. Also, this method uses a wide range of feature set to recognize particles, which is suitable to different type of particles and different image qualities. The experiment results show it has advantage on the generability.
4. As to the problem of tilting alignment, a TEM tilting alignment optimization model based on local cross-correlation is established in this paper. This model can do well in noisy TEM images. And it also has good extensibility, which means it can change the similarity evaluation function freely without the change of the model. Experiments show that this method has more accurate aligning result than the global cross-correlation method.
5. To obtain the optimum, it is needed to search the whole transform coefficients space, which is time-consuming. This paper proposes an adaptive searching mechanism according to the relativity between two consecutive tilt images, which can greatly reduce searching range. Experiment shows that it can enhance efficiency by 70% at least.
在冷冻电镜图像三维重构过程中,面临三大关键问题:(1)冷冻电镜生物大分子图像分割问题。冷冻电镜图像分割对于冷冻电镜图像高层次的特征提取、目标识别和图像配准等至关重要。但由于其低对比度和低信噪比等特点,很难达到理想的分割效果。(2)冷冻电镜图像生物大分子颗粒识别问题。生物大分子颗粒识别是冷冻电镜单粒子三维重构方法非常重要的环节,直接影响三维重构效率和三维结构精度。从低对比度和低信噪比的冷冻电镜图像中提取成千上万个生物大分子颗粒是一件相当耗时的工作,已经成为三维重构过程的瓶颈,自动颗粒识别方法的研究已成为当前关注的焦点。(3)电子断层扫描旋转图像配准问题。在电子断层扫描图像拍摄过程中,由于样本转动轴在旋转时的机械误差导致了旋转过程中样本的移动,直接影响三维结构的精度。因此,电子断层扫描旋转图像配准是电子断层扫描三维重构的关键环节。
本论文围绕如何利用现代计算机技术以及数字图像处理技术提高冷冻电镜图像三维重构的效率和三维的结构精度开展了深入分析和系统研究,取得了如下的研究成果:
1.针对冷冻电镜生物大分子图像的低信噪比和低对比度等特点,提出了基于小波变换和高斯差分的冷冻电镜生物大分子图像分割算法。该算法运用小波变换对冷冻电镜图像进行多尺度分解后,对低分辨率图像计算高斯差分图像,并对其进行基于灰度梯度的阈值分割,最后基于梯度信息进一步将低分辨率图像分割结果逐层过渡到高分辨率原图像上。该算法可以有效地抑制噪声,区分不同尺度的边缘信号,与canny算法以及最大类间方差方法的实验结果对比,该算法在主观评价和客观定量分析上均表现出更好的分割效果。
2.本文针对冷冻电镜图像颗粒区域难以提取的问题,提出了基于高斯差分掩模的形状特征提取算法。该算法通过计算颗粒样本的高斯差分图像掩膜,提取目标对象的近似区域进行形状特征提取。通过实验与基于方差图像的方法相比,该算法所提取的形状特征的单一识别率更高,具有更好的鉴别能力和分类性能。
3.针对冷冻电镜图像质量差、颗粒形状复杂多样的特点,提出了基于偏最小二乘的冷冻电镜图像颗粒识别二步方法。该二步方法包括基于高斯函数的候选颗粒检测和基于偏最小二乘的候选颗粒识别两个步骤。该方法运用基于PLS的识别机制,具有小样本学习特点,只需少量样本,就能保证在保持良好的正选率情况下,错选率很低,实验结果表明,该方法对方形颗粒的错选率为14%,对圆形颗粒的错选率只有7%。其次,采用了多种类型特征集合,可以对不同颗粒形状进行识别。实验结果验证了该方法具有通用性强,识别率高等特点。与同类算法相比,该方法在错选率方面具有优势。
4.针对电子断层扫描图像受噪声污染严重的特点,提出了基于局部互相关的电子断层扫描旋转图像配准优化模型。该模型克服了传统全局互相关方法固有的对噪声敏感的局限性,使配准结果更精确。该模型还具有良好的可扩展性,即可在此模型基础上,灵活定义相似度评价函数而无需改变整个算法框架。实验结果显示,该方法比同类基于全局互相关的方法表现出更好的配准精确度。
5.为了获得优化模型的最优解,需要对整个变换系数空间进行搜索,使算法效率非常低。根据图像序列前后图像偏移的相关性,提出了电子断层扫描旋转图像配准优化模型的自适应搜索机制。该机制能缩小变换方程系数空间的搜索范围,使配准过程能快速有效的进行。实验数据显示,使用该搜索机制,算法效率至少可以提高70%。
Mordern computer technology has made a significant contribution to the study of structural biology on expressing, storing, acquiring and analyzing structural information. Structural biology is one of the most important subjects of life sciences. Its key research content, the three-dimensional (3D) structure of biological macromolecules, is the premise and foundation of studying the relationship between macromolecule structure and function. Determining high resolution 3D structure of macromolecule is significant to understanding its corresponding function. Electron cryomicroscopy (Cryo-EM) is one of the main methods to obtain the macromolecules structure.
There are three key issues which is crucial to Cryo-EM 3D reconstruction. The first problem is segmentation of Cryo-EM image, which is crucial to the high-level processing of electron micrograph, including feature extraction, particle recognition, image registration and so on. But Cryo-EM image has the characteristic of low contrast and low signal-to-noise ratio (SNR) and is difficult to acquire ideal segmentation effect. The second is macromolecule particle recognition from Cryo-EM image, which is a crucial step of single particle reconstruction and has directly effect on the efficiency of Cryo-EM 3D reconstruction and the resolution of 3D structure. Selecting hundreds of thousands of particles from low contrast and low SNR Cryo-EM image is one of the major bottle-necks in advancing toward achieving atomic resolution reconstruction of biological macromolecules. The third problem is alignment of transmission electron microscope tilting series. Mechanical inaccuracies of the specimen holder cause specimen movement during tilting, which requires appropriate compensating adjustment. Accurate image alignment is needed for computing three-dimensional reconstructions from transmission electron microscope (TEM) tilt series.
This paper has made deeply research on how to use modern computer technology and image processing technology to enhance the efficiency of CryoEM three-dimensional reconstruction and the resolution of 3D structure. Several finding has been made, which is as follows.
1. This paper provides a new Cryo-EM image segmentation method based on wavelet transform and difference of Gaussian (DoG), which performs multi-scale decomposition based on wavelet transform at first. And then this method gets the DoG image of low-resolution image and does threshing segmentation based on the combination of gray and gradient information. At last, the segmentation result in low-resolution image is transferred to the high-resolution image based on the gradient. This method can overcome the flaw of the low SNR, and experiments show that it can get better segmentation effect than the canny algorithm and OTSU method.
2. It is difficult to get particle region in Cryo-EM images, which leads to the difficulty of shape fearture extraction. This paper proposed a shape feature extraction algorithm based on DoG mask. This algorithm only obtains the approximate region of objects, from which the shape features are extracted. Experiments show the shape feature extracted by this method has better ability of classification than the method based on variance image.
3. As to the problem of particle recognition, a two step approach for the particle recognition from Cryo-EM images has been developed in this paper, which is based on the principle of partial least square (PLS). This approach involves two steps: the detection of candidate particles based on Gaussian function and the recognition from these candidates based on PLS. This method is characterized by small sample study and can obtain satisfied recognition result with a few samples needed. Experiments show it can get low error-select-rate, which is 14% while selecting rectangular particle and only 7% while selecting circle particle. Also, this method uses a wide range of feature set to recognize particles, which is suitable to different type of particles and different image qualities. The experiment results show it has advantage on the generability.
4. As to the problem of tilting alignment, a TEM tilting alignment optimization model based on local cross-correlation is established in this paper. This model can do well in noisy TEM images. And it also has good extensibility, which means it can change the similarity evaluation function freely without the change of the model. Experiments show that this method has more accurate aligning result than the global cross-correlation method.
5. To obtain the optimum, it is needed to search the whole transform coefficients space, which is time-consuming. This paper proposes an adaptive searching mechanism according to the relativity between two consecutive tilt images, which can greatly reduce searching range. Experiment shows that it can enhance efficiency by 70% at least.
引文
[1]梁毅.结构生物学[M].北京:科学出版社, 2005
[2]Ilari A, Savino C. Protein structure determination by x-ray crystallography[J]. Methods Mol Biol, 2008, 452:63-87.
[3]林亚静,刘志杰,龚为民.蛋白质结构研究[J].生命科学, 2007, 19(3): 289-293
[4]Foster MP, McElroy CA, Amero CD. Solution NMR of large molecules and assemblies [J]. Biochemistry, 2007, 46(2):331-40.
[5]Sprangers R, Velyvis A, Kay LE. Solution NMR of supramolecular complexes: providing new insights into function[J]. Nat Methods, 2007, 4(9):697-703.
[6]DeRosier D., Klug A. Reconstruction of 3-dimensional structures from electron micrographs[J], Nature, 1968, 217: 130-134
[7]Boekema EJ, Folea M, Kou?il R. Single particle electron microscopy[J]. Photosynth Res, 2009, Jun 10, [Epub ahead of print]
[8]Bourne P.E., Weissig H. Structural Bioinformatics[M].刘振明,刘海燕, et al.北京:化学工业出版社, 2009
[9]Teichmann SA, Murzin AG, Chothia C. Determination of protein function, evolution and interactions by structural genomics[J]. Curr. Opin. Struct Biol., 2001, 11(3):354-363
[10]Taylor K, Glaeser RM. Electron diffraction of frozen, hydrated protein crystals[J]. Science, 1974, 186:1036–37
[11]Yu, X., Jin, L., et al. 3.88 ? structure of cytoplasmic polyhedrosis virus by cryo-electron microscopy[J]. Nature, 2008, 453 (7193): 415-419
[12]Zhang X, Settembre E, Xu C, et al. Near-atomic resolution using electron cryomicroscopy and single-particle reconstruction[J]. Proc. Natl. Acad. Sci. USA, 2008, 105 (6): 1867-1872
[13]徐伟,潘东日.单颗粒生物大分子结构的富里叶变换三维重建法[J].电子显微学报, 1992(6): 415-422
[14]Liu H, Cheng L, Zeng S, et al. Symmetry-adapted spherical harmonics method for high-resolution 3D single-particle reconstructions[J]. J Struct Biol., 2008,161(1):64-73
[15]Joachim Frank. Single-particle Imaging of Macromolecules by Cryo-electron Microscopy[J]. Annual Review of Biophysics and Biomolecular Structure, 2002, 31: 303-319
[16]Vladan Lu?i?, Friedrich F?rster, Wolfgang Baumeister. Structural Studies by Electron Tomography: From Cells to Molecules[J]. Annu. Rev. Biochem, 2005, 74: 833-65
[17]James R. Kremer, David N. Mastronarde, J. Richard Mcintosh. Computer Visualization of Three-Dimensional Image Data Using IMOD[J]. Journal of Structural Biology, 116, 1996, pp.71–76
[18]David N. Mastronarde. Dual-Axis Tomography: An Approach with Alignment Methods That Preserve Resolution[J]. Journal of Structural Biology, 1997, 120: 343-352
[19]Stephan Nickell, Friedrich Foster, Alexandros Linaroudis, et al. TOM software toolbox: acquisition and analysis for electron tomography[J]. Journal of Structural Biology, 2005, 149: 227-234
[20]Shawn Q. Zheng, Bettina Keszthelyi, Eric Branlund, et al. UCSF tomography: An integrated software suite for real-time electron microscopic tomographic data collection, alignment, and reconstruction[J]. Journal of Structural Biology, 2007, 157: 138-147
[21]Sonka M, Hlavac V, Boyle R. Image Processing, Analysis, and Machine Vision[M]. Beijing: Posts and Telecom Press, 2003
[22]王鹏伟.基于多尺度理论的图像分割方法研究[D].合肥:中国科学技术大学, 2007
[23]L.G.Roberts. Machine Perception of three dimension Solids[A]. Optimal and Electro-optimal Information Processing[C]. MA: MIT Press, 1965:99-197
[24]季虎,孙即祥,邵晓芳,等.图像边缘提取方法及展望.计算机工程与应用, 2004, 14: 70-73
[25]Gonzalez R C, Woods R E. Digital Image Processing[M].阮秋琦,阮宇智.北京:电子工业出版社, 2003
[26] Dong Hu, Xiangzhong Tian. A Multi-Directions Algorithm for Edge Detection Based on Fuzzy Mathematical Morphology[A]. Proceedings of 16th International Conference on Artificial Reality and Telexistence[C]. Washington. IEEE Computer Society, 2006:361-364
[27]赵继印,徐艳蕾,焦玉斌.基于顺序形态学的图像边缘检测快速算法的研究[J].电子学报, 2008, 36(11): 2195-2199
[28]S.Konishi, A. Yuille, J. Coughlan. A statistical approach to multi-scale edge detection[J]. Image and Vision Computing, 2003, (21):37-485
[29]Weiqing Li, Chengbiao Wang, Qun Wang, et al. An Edge Detection Method Based on Optimized BP Neural Network[A]. Proc. of the 2008 International Symposium on Information Science and Engineering[C]. Washington: IEEE Computer Society, 2008: 40- 44
[30]D.S.Kim, W.H. Lee, I.S. Kweon. Automatic edge detection using 3*3 ideal binary pixel patterns and fuzzy-based edge thresholding[J]. Pattern Recognition Letters. 2004, (25):101-106
[31]何鹏,徐春伟,张裕.适用于医学图像的模糊边缘检测改进算法[J].计算机工程与应用. 2009, 45(18): 231-233
[32]Kun Wang, Liqun Gao, Zhaoyu Pian, et al. Edge Detection Combined Entropy Threshold and Self-Organizing Map (SOM)[A]. 4th International Symposium on Neural Networks[C]. Berlin: Springer-Verlag, 2007: 931-937
[33]姜兴乾,李星野,贾淑华.基于最大熵与小波变换的图像边缘检测算法[J].微电子学与计算机. 2009, 26(3): 189-191
[34]王建锋,吴庆标.分层遗传算法实现图像边缘检测[J].计算机工程与应用2006,14: 95-96
[35]薛东辉,朱耀庭,朱光喜,等.基于尺度分维的图象边缘检测方法研究[J].华中理工大学学报, 1996, 24(8):37-39
[36]Harauz G., Fong-Lochovsky A. Automatic selection of macromolecules from electron micrographs by component labelling and symbolic processing[J]. Ultramicroscopy, 1989, 31: 333-344
[37]David Woolford, Ben Hankamer, Geoffery Ericksson. The Laplacian of Gaussian and arbitrary z-crossings approach applied to automated single particle reconstruction[J]. Journal of Structural Biology, 2007, 159 (1): 122-134
[38] Octavio Gómez, González J A, Morales E F. Image Segmentation Using Automatic Seeded Region Growing and Instance-Based Learning[A]. Progress in Pattern Recognition, Image Analysis and Applications[C]. Berlin: Springer-Verlag, 2008: 192-201
[39]杨文明,陈国斌,沈晔湖.一种基于分水岭变换的图像分割方案[J].浙江大学学报, 2006, 40(9): 1503-1506
[40]李旭超,朱善安.图像分割中的马尔可夫随机场方法综述[J].中国图象图形学报, 2007, 12(5):789-798
[41]P. S. Umesh Adiga, Ravi Malladi, William Baxter, et al. A binary segmentation approach for boxing ribosome particles in cryo EM micrographs[J]. Journal of Structural Biology, 2004, 145: 142-151
[42]Umesh Adiga, William T. Baxter, Richard J. Hall. Particle picking by segmentation: A comparative study with SPIDER-based manual particle picking[J]. Journal of Structural Biology, 2005, 152(3): 211-220
[43]Robert M.Glaeser. Historical background: why is it important to improve automated particle selection methods? [J]. J. Structure Biology, 2004,145: 15-18
[44]Yuanxin Zhu, Bridget Carragher, Robert M Glaeser, et al. Automatic particle selection: results of a comparative study[J]. J. Structure Biology, 2004, 145: 3-14
[45]William V Nicholson, Robert M Glaeser. Review: automatic particle detection in electron microscopy[J]. J. Structure Biology, 2001, 133: 90-101
[46]Saad A, Chiu W, Thuman-Commike P. Multiresolution approach to automatic detection of spherical particles from electron cryomicroscopy images[A]. Proc. 1998 Int. Conf. Image Process[C]. 1998, 3: 846-850
[47]Ludtke, S J, Baldwin P R, Chiu W. EMAN: Semiautomated software for high resolution single particle reconstruction[J]. J. Struct. Biol. 1999, 28: 82-97
[48]Zhong Huang, Pawel A Pencek. Application of template matching technique to particle detection in electron micrograhps[J]. J. Structure Biology, 2004, 145: 29-40
[49]Stoschek A, Hegerl R. Automated detection of macromolecules from electron micrographs using advanced filter techniques[J]. J. Microsc. (Oxford), 1997, 185: 76-94
[50]Alan M. Roseman. Particle finding in electron micrographs using a fast local correlation algorithm[J]. Ultramicroscopy, 2003, 94: 225-236
[51]A. M. Roseman. FindEM――a fast, efficient program for automatic selection of particles from electron micrographs[J]. J. Structure Biology, 2004, 145: 91-99
[52]Vibhor Kumar, Jukka Heikkonen, Peter Engelhardt, et al. Robust filtering and particle picking in micrograph images towards 3D reconstruction of purified proteins with cryo-electron microscopy[J]. J. Structure Biology, 2004, 145: 41-51
[53]Fred J Sigworth. Classical detection theory and the cryo-EM particle selection problem[J]. J. Structure Biology, 2004, 145: 111-122
[54]H. Chi Wong, Jindong Chen, Fabrice Mouche, et al. Model-based particle picking for cryo-electron microscopy[J]. J. Structure Biology, 2004, 145: 157-167
[55]Tanvir R Shaikh, Ramon Trujillo, Jamie S LeBarron, et al. Particle-verification for single-particle, reference-based reconstruction using multivariate data analysis and classification[J]. Journal of Structural Biology, 2008, 164(1): 41-48
[56]Chen J Z, Grigorieff N. SIGNATURE: a single-particle selection system for molecular electron microscopy. J. Struct. Biol[J]. 2007, 157 (1): 168-173
[57]Yuanxin Zhu, Bridge Carragher, David J. Kriegman, et al. Automated identification of filaments in cryoelectron microscopy images[J]. J. Structure Biology, 2001, 135: 302-312
[58]Zeyun Yu, Chandraijit Bajaj. Detecting circular and rectangular particles based on geometric feature detection in electron micrographs[J]. J. Structure Biology, 2004, 145: 168-180
[59]Zhu Y, Carragher B, Mouche F, et al. Automatic particle detection through efficient hough transforms[J]. IEEE Transactions on Medical Imaging, 2003, 22(9): 1053-1062
[60]Boier Martin I M, Marinescu D C, Lynch R E, et al. Identification of spherical virus particles in digitized images of entire electron micrographs[J]. J. Struct. Biol.1997,120: 146-157
[61]Kivioja T, Ravantti J, Verkhovsky A, et al. Local average intensity-based method for identifying spherical particles in electron micrographs[J]. J. Struct. Biol., 2000, 131: 126-134
[62]Lata K R, Penczek P, Frank J. Automatic particle picking from electron micrographs[J]. Ultramicroscopy, 1995, 58: 381-391
[63]Richard J. Hall, Ardan Patwardhan. A two step approach for semi-automated particle selection from low contrast cryo-electron micrographs[J]. J. Structure Biology, 2004, 145: 19-28
[64]Mallick S. P., Yuanxin Zhu, David Kriegman. Detecting particles in cryo-EM micrographs using learned features[J]. J. structure Biology, 2004, 145: 52-62
[65]Niels Volkmann. An approach to automated particle picking from electron micrographs based on reduced representation templates[J]. J. Structure Biology, 2004, 145: 152-156
[66]CO Sorzano, E Recarte, M Alcorlo, et al. Automatic particle selection from electron micrographs using machine learning techniques[J]. Journal of Structural Biology, 2009, 167(3):252-260
[67]Toshihiko Ogura, Chikara Sato. An automatic particle pickup method using a neural network applicable to low-contrast electron micrographs[J]. J. Structure Biology, 2001, 136: 227-238
[68]Toshihiko Ogura, Chikara Sato. Automatic particle pickup method using a neural network has high accuracy by applying an initial weight derived from eigenimages: a new reference free method for single-particle analysis[J]. J. Structure Biology, 2004, 145: 63-75
[69]A. pascual-Montano, L E Donate, M Valle, M. Bàrcena, et al. A novel neural network technique for analysis and classification of EM single-particle images[J]. J. Structure Biology, 2001, 133: 233-245
[70]Toshihiko Ogura, Chikara Sato. Auto-accumulation method using simulated annealing enables fully automatic particle pickup completely free from a matching template or learned data[J]. J. Structure Biology, 2004, 146: 344-358
[71]J R Plaisier, R I Koning, H K Koerten, et al. TYSON: Robust searching, sorting, and selecting of single particles in electron micrographs[J]. J. Structure Biology, 2004, 145: 76-83
[72]B.K. Rath, J. Frank. Fast automatic particle picking from cryo-electron micrographs using a locally normalized cross-correlation function: a case study[J]. J. Structure Biology, 2004, 145: 84-90
[73]N R Voss, C K Yoshioka, M Radermacher, et al. DoG Picker and TiltPicker: Software tools to facilitate particle selection in single particle electron microscopy[J]. Journal of Structural Biology, 2009, 166(2): 205-213
[74]David Woolford, Geoffery Ericksson, Rosalba Rothnagel, et al. SwarmPS: Rapid, semi-automated single particle selection software[J]. Journal of Structural Biology, 2007, 157(1): 174-188
[75]J.R. Plaisier, L. Jiang, J.P. Abrahams,Cyclops: New modular software suite for cryo-EM[J]. Journal of Structural Biology, 2007, 157(1): 19-27
[76]彭士纯.基于多尺度分析的医学图像配准与融合方法研究[D].湖北:华中科技大学博士学位论文, 2006
[77]Qingxiong S Zheng, Michael B Braunfeld, John W Sedat, et al. An improved strategy for automated electron microscopic tomography[J] Journal of Structural Biology, 2004,147: 91–101
[78]David N Mastronarde. Automated electron microscope tomography using robust prediction of specimen movements[J]. Journal of Structural Biology, 2005, 152: 36-51
[79]Frank J, McEwen B F. Alignment by cross-correlation[A]. Electron Tomography: Three Dimensional Imaging with the Transmission Electron Microscope[C]. Plenum, New York, 1992, 8: 205–213
[80]Yu Liu, Pawel A Penczek, Bruce F McEwen, et al. A marker-free alignment method for electron tomography[J]. Ultramicroscopy, 1995, 58: 393-402
[81]Hanspeter Winkler, Kenneth A Taylor. Accurate marker-free alignment with simultaneous geometry determination and reconstruction of tilt series in electron tomography[J]. Ultramicroscopy, 2006, 106: 24-254
[82]Cameron H Owen, Willian JLandis. Alignment of electron tomographic bycorrelation without the use of gold particles[J]. Ultramicroscopy, 1996, 63: 27-38
[83]Sami Brandt, Jukka Heikkonen, Peter Engelhant. Automatic Alignment of Transmission Electron Microscope Tilt Series Without Fiducial Markers[J]. Journal of Structural Biology, 2001, 136: 201-213
[84]Díez DC, Al-Amoudi A, Glynn A-M, et al. Fiducial-less Alignment of Cryo-Sections[J]. Journal of Structural Biology, 2007, 159: 413-423
[85]Sami Brandt, Jukka Heikkonen, Peter Engelhant. Multiphase Method for Automatic Alignment of Transmission Electron Microscope Images Using Markers[J]. Journal of Structural Biology, 2001, 133: 10-22
[86]P Tchelidze, C Sauvage, N Bonnet, L Kilian, et al. Electron tomography of amplified nanogold immunolabelling: Improvement of quality based on alignment of projections with sonograms and use of post-reconstruction deconvolution[J]. Journal of Structural Biology, 2006, 156: 421-431
[87]Penczek P, Marko M, Buttle K, et al. Double-tilt electron tomography[J]. Ultramicroscopy, 1995, 60: 393-410
[88]Díez D C, Anja Seybert, Achilleas S Frangakis. Tilt-series and electron microscope alignment for the correction of the non-perpendicularity of beam and tilt-axis[J]. Journal of Structural Biology, 2006, 154: 195-205
[89]Ress D, Harlow M L, Schwarz M, et al. Automatic acquisition of fiducial markers and alignment of images in tilt series for electron tomography[J]. J. Electron Microsc. 1999, 48(3): 277-287
[90]Benveniste A, Nikoukhah R, willsky A S. Multiscale system theory[A]. Proc.29th IEEE conf. on Decision and Control[C]. IEEE :Honolulu, 1990: 2484-2487
[91]Willsky A S. Multiresolution Markov models for signal and image processing[J]. IEEE Trans., 2002, 90(8): 1396-1458
[92]Benveniste A, Nikoukhah R, willsky A S. Multiscale System Theory[J]. IEEE Trans. Fundamental Theory and Applications, 1994, 14(1): 2-14
[93]Ge Ji, Wang Yaonan, Zhou Bowen, et al. Multi-Scale Shaft Parts Image Edge Detection and Measurement Based on Wavelet Transform[A]. Proceedings of the 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and IndustrialApplication[C], 2008: 371-375
[94]Pennec E L, Mallat S. Sparse geometric image representation with bandelets[J]. IEEE Trans. Image Processing, 2005, 14(4): 423-438
[95]Qiming L, Khoshgoftaar T M. Unsupervised multiscale color image segmentation based on MDL principle[J]. IEEE Trans. Image Processing, 2006, 15(9): 2755-2761
[96]李军宏,潘泉,陈玉春等.基于Radon变换的纹理图像多尺度不变量分析算法[J].中国图象图形学报, 2005, 10(9): 1117-1123
[97]焦李成,孙强.多尺度变换域图像的感知与识别:进展和展望[J].计算机学报, 2006, 29(2): 177-193
[98]Witkin A P. Scale space filtering: A new approach to multiscale description[A]. Proc. Image Understanding[C], 1984, 1(1): 79-95
[99]Tomasi C, Manduchi R. Bilateral filtering for gray and color images [A]. In: Proceedings of the Sixth International Conference on Computer Vision [C]. Washington, DC, USA: IEEE Computer Society, 1998: 839-846
[100]Nguyen D V, Recke D M. Tumor classification by partial least squares using microarray gene expression data[J], Bioinformatics, 2002, 13(1): 39-50
[101]Jangsun Baekaf, Minsoo Kim. Face recognition using partial least squares components[J]. Pattern Recognition, 2004, 37(6): 1303-1306
[102]Jie Song, Jiaxing Cheng, Zhongwei Xu. Face Recognition Based on Covering Algorithm and PLS[A]. Proceedings of the 6th World Congress on Intelligent Controland Automation[C], Dalian, 2006: 21-23
[103]楼安平,杨新.基于偏最小二乘法与支持向量机的人脸识别[J].计算机仿真, 2005, 22(12): 166-168
[104]孙宁,冀贞海,邹采荣,等.基于2维偏最小二乘法的图像局部特征提取及其在面部表情识别中的应用[J].中国图象图形学报, 2007, 12(5): 847-853
[105]王惠文.偏最小二乘回归方法及其应用[M].北京:国防工业出版社, 1999
[106]Wold S, Martens H, Wold H. The Multivariate Calibration Problem in Chemistry Solved by the PLS method[A]. Proceedings of Conference on Matrix Pencils,Lecture Notes in mathematics[C], Springer, Heidelberg,1983,286-293
[107]Wold S, Trygg J, Berglund A, et al. Some recent developments in PLSmodeling[J]. Chemomertics and lntelligent Laboratory Systems, 2001, 58(1):131-150
[108]H?skuldsson A. PLS Regression Methods[J]. Journal of Chemometrics, 1988, 2:211-228
[109]H?skuldsson A. Causal and path modeling[J]. Chemometrics and Intelligent Laboratory Systems, 2001, 58:287-311
[110]孙权森.基于相关投影分析的特征抽取与图像识别研究[D].南京:南京理工大学, 2006
[111]Baek J, Kim M. Face recognition using partial least squares components[J]. Pattern recognition, 2004, 37(6): 1303-1306
[112]Noeairi H, Mostafa Q E, Vigneau E, et al. Discrimination on latent components wih respect to patterns: Application to multicollinear data[J]. Computational Statistics & Data Analysis, 2005, 48:139-147
[113]Rosipal R, Trejo L J, Matthews B. Kenrel PLS-SVC for Linear and Nonlinear Classification[A]. Porceedings of the Twentieth International Conference on Machine Learning(ICML)[C], Washington DC, 2003: 640-647
[114]Rosipal R. Kernel Partial Least Squares for Nonlinear Regression and Discrimination[J]. Neural Network World, 2003, 13(3):291-300
[115]C-M Pun, M-C Lee. Log-polar wavelet energy signatures for rotation and scale invariant texture classification[J]. IEEE Trans. Pattern Anal. Mach. Intell., 2003, 25(5): 590-603
[116] Peiling Cui, Junhong Li, Quan Pan,et al. Rotation and scaling invariant texture classification based on Radon transform and multiscale analysis[J]. Pattern Recognition Letters, 2006, 27(5): 408-413
[117]K Jafari-Khouzani, H Soltanian-Zadeh. Rotation Invariant Multiresolution Texture Analysis Using Radon and Wavelet Transforms[J]. IEEE Tran on Image Process, 2005, 14(6): 783-794
[118]Pizer S M, Koenderink J J, Lifshitz L M, et al. An image description for object definition based on external regions in the stack[J]. Proc. of 9th Conf. Information Processing in Medical Imaging. Boston, MA: Martinus Nijhoff, 1986
[119]M K Hu,Visual pattern recognition by moment invariants[J]. IEEE Transactionson Information Theory, 1962, 8: 179-182
[120]卢振泰,冯衍秋,冯前进,等.基于等效子午面与互信息量的医学图像配准[J].计算机学报, 2009, 32(8): 1611-1617
[121]杨金宝.基于灰度相似性测度的医学图像配技术研究[D].山东:山东大学, 2008
[2]Ilari A, Savino C. Protein structure determination by x-ray crystallography[J]. Methods Mol Biol, 2008, 452:63-87.
[3]林亚静,刘志杰,龚为民.蛋白质结构研究[J].生命科学, 2007, 19(3): 289-293
[4]Foster MP, McElroy CA, Amero CD. Solution NMR of large molecules and assemblies [J]. Biochemistry, 2007, 46(2):331-40.
[5]Sprangers R, Velyvis A, Kay LE. Solution NMR of supramolecular complexes: providing new insights into function[J]. Nat Methods, 2007, 4(9):697-703.
[6]DeRosier D., Klug A. Reconstruction of 3-dimensional structures from electron micrographs[J], Nature, 1968, 217: 130-134
[7]Boekema EJ, Folea M, Kou?il R. Single particle electron microscopy[J]. Photosynth Res, 2009, Jun 10, [Epub ahead of print]
[8]Bourne P.E., Weissig H. Structural Bioinformatics[M].刘振明,刘海燕, et al.北京:化学工业出版社, 2009
[9]Teichmann SA, Murzin AG, Chothia C. Determination of protein function, evolution and interactions by structural genomics[J]. Curr. Opin. Struct Biol., 2001, 11(3):354-363
[10]Taylor K, Glaeser RM. Electron diffraction of frozen, hydrated protein crystals[J]. Science, 1974, 186:1036–37
[11]Yu, X., Jin, L., et al. 3.88 ? structure of cytoplasmic polyhedrosis virus by cryo-electron microscopy[J]. Nature, 2008, 453 (7193): 415-419
[12]Zhang X, Settembre E, Xu C, et al. Near-atomic resolution using electron cryomicroscopy and single-particle reconstruction[J]. Proc. Natl. Acad. Sci. USA, 2008, 105 (6): 1867-1872
[13]徐伟,潘东日.单颗粒生物大分子结构的富里叶变换三维重建法[J].电子显微学报, 1992(6): 415-422
[14]Liu H, Cheng L, Zeng S, et al. Symmetry-adapted spherical harmonics method for high-resolution 3D single-particle reconstructions[J]. J Struct Biol., 2008,161(1):64-73
[15]Joachim Frank. Single-particle Imaging of Macromolecules by Cryo-electron Microscopy[J]. Annual Review of Biophysics and Biomolecular Structure, 2002, 31: 303-319
[16]Vladan Lu?i?, Friedrich F?rster, Wolfgang Baumeister. Structural Studies by Electron Tomography: From Cells to Molecules[J]. Annu. Rev. Biochem, 2005, 74: 833-65
[17]James R. Kremer, David N. Mastronarde, J. Richard Mcintosh. Computer Visualization of Three-Dimensional Image Data Using IMOD[J]. Journal of Structural Biology, 116, 1996, pp.71–76
[18]David N. Mastronarde. Dual-Axis Tomography: An Approach with Alignment Methods That Preserve Resolution[J]. Journal of Structural Biology, 1997, 120: 343-352
[19]Stephan Nickell, Friedrich Foster, Alexandros Linaroudis, et al. TOM software toolbox: acquisition and analysis for electron tomography[J]. Journal of Structural Biology, 2005, 149: 227-234
[20]Shawn Q. Zheng, Bettina Keszthelyi, Eric Branlund, et al. UCSF tomography: An integrated software suite for real-time electron microscopic tomographic data collection, alignment, and reconstruction[J]. Journal of Structural Biology, 2007, 157: 138-147
[21]Sonka M, Hlavac V, Boyle R. Image Processing, Analysis, and Machine Vision[M]. Beijing: Posts and Telecom Press, 2003
[22]王鹏伟.基于多尺度理论的图像分割方法研究[D].合肥:中国科学技术大学, 2007
[23]L.G.Roberts. Machine Perception of three dimension Solids[A]. Optimal and Electro-optimal Information Processing[C]. MA: MIT Press, 1965:99-197
[24]季虎,孙即祥,邵晓芳,等.图像边缘提取方法及展望.计算机工程与应用, 2004, 14: 70-73
[25]Gonzalez R C, Woods R E. Digital Image Processing[M].阮秋琦,阮宇智.北京:电子工业出版社, 2003
[26] Dong Hu, Xiangzhong Tian. A Multi-Directions Algorithm for Edge Detection Based on Fuzzy Mathematical Morphology[A]. Proceedings of 16th International Conference on Artificial Reality and Telexistence[C]. Washington. IEEE Computer Society, 2006:361-364
[27]赵继印,徐艳蕾,焦玉斌.基于顺序形态学的图像边缘检测快速算法的研究[J].电子学报, 2008, 36(11): 2195-2199
[28]S.Konishi, A. Yuille, J. Coughlan. A statistical approach to multi-scale edge detection[J]. Image and Vision Computing, 2003, (21):37-485
[29]Weiqing Li, Chengbiao Wang, Qun Wang, et al. An Edge Detection Method Based on Optimized BP Neural Network[A]. Proc. of the 2008 International Symposium on Information Science and Engineering[C]. Washington: IEEE Computer Society, 2008: 40- 44
[30]D.S.Kim, W.H. Lee, I.S. Kweon. Automatic edge detection using 3*3 ideal binary pixel patterns and fuzzy-based edge thresholding[J]. Pattern Recognition Letters. 2004, (25):101-106
[31]何鹏,徐春伟,张裕.适用于医学图像的模糊边缘检测改进算法[J].计算机工程与应用. 2009, 45(18): 231-233
[32]Kun Wang, Liqun Gao, Zhaoyu Pian, et al. Edge Detection Combined Entropy Threshold and Self-Organizing Map (SOM)[A]. 4th International Symposium on Neural Networks[C]. Berlin: Springer-Verlag, 2007: 931-937
[33]姜兴乾,李星野,贾淑华.基于最大熵与小波变换的图像边缘检测算法[J].微电子学与计算机. 2009, 26(3): 189-191
[34]王建锋,吴庆标.分层遗传算法实现图像边缘检测[J].计算机工程与应用2006,14: 95-96
[35]薛东辉,朱耀庭,朱光喜,等.基于尺度分维的图象边缘检测方法研究[J].华中理工大学学报, 1996, 24(8):37-39
[36]Harauz G., Fong-Lochovsky A. Automatic selection of macromolecules from electron micrographs by component labelling and symbolic processing[J]. Ultramicroscopy, 1989, 31: 333-344
[37]David Woolford, Ben Hankamer, Geoffery Ericksson. The Laplacian of Gaussian and arbitrary z-crossings approach applied to automated single particle reconstruction[J]. Journal of Structural Biology, 2007, 159 (1): 122-134
[38] Octavio Gómez, González J A, Morales E F. Image Segmentation Using Automatic Seeded Region Growing and Instance-Based Learning[A]. Progress in Pattern Recognition, Image Analysis and Applications[C]. Berlin: Springer-Verlag, 2008: 192-201
[39]杨文明,陈国斌,沈晔湖.一种基于分水岭变换的图像分割方案[J].浙江大学学报, 2006, 40(9): 1503-1506
[40]李旭超,朱善安.图像分割中的马尔可夫随机场方法综述[J].中国图象图形学报, 2007, 12(5):789-798
[41]P. S. Umesh Adiga, Ravi Malladi, William Baxter, et al. A binary segmentation approach for boxing ribosome particles in cryo EM micrographs[J]. Journal of Structural Biology, 2004, 145: 142-151
[42]Umesh Adiga, William T. Baxter, Richard J. Hall. Particle picking by segmentation: A comparative study with SPIDER-based manual particle picking[J]. Journal of Structural Biology, 2005, 152(3): 211-220
[43]Robert M.Glaeser. Historical background: why is it important to improve automated particle selection methods? [J]. J. Structure Biology, 2004,145: 15-18
[44]Yuanxin Zhu, Bridget Carragher, Robert M Glaeser, et al. Automatic particle selection: results of a comparative study[J]. J. Structure Biology, 2004, 145: 3-14
[45]William V Nicholson, Robert M Glaeser. Review: automatic particle detection in electron microscopy[J]. J. Structure Biology, 2001, 133: 90-101
[46]Saad A, Chiu W, Thuman-Commike P. Multiresolution approach to automatic detection of spherical particles from electron cryomicroscopy images[A]. Proc. 1998 Int. Conf. Image Process[C]. 1998, 3: 846-850
[47]Ludtke, S J, Baldwin P R, Chiu W. EMAN: Semiautomated software for high resolution single particle reconstruction[J]. J. Struct. Biol. 1999, 28: 82-97
[48]Zhong Huang, Pawel A Pencek. Application of template matching technique to particle detection in electron micrograhps[J]. J. Structure Biology, 2004, 145: 29-40
[49]Stoschek A, Hegerl R. Automated detection of macromolecules from electron micrographs using advanced filter techniques[J]. J. Microsc. (Oxford), 1997, 185: 76-94
[50]Alan M. Roseman. Particle finding in electron micrographs using a fast local correlation algorithm[J]. Ultramicroscopy, 2003, 94: 225-236
[51]A. M. Roseman. FindEM――a fast, efficient program for automatic selection of particles from electron micrographs[J]. J. Structure Biology, 2004, 145: 91-99
[52]Vibhor Kumar, Jukka Heikkonen, Peter Engelhardt, et al. Robust filtering and particle picking in micrograph images towards 3D reconstruction of purified proteins with cryo-electron microscopy[J]. J. Structure Biology, 2004, 145: 41-51
[53]Fred J Sigworth. Classical detection theory and the cryo-EM particle selection problem[J]. J. Structure Biology, 2004, 145: 111-122
[54]H. Chi Wong, Jindong Chen, Fabrice Mouche, et al. Model-based particle picking for cryo-electron microscopy[J]. J. Structure Biology, 2004, 145: 157-167
[55]Tanvir R Shaikh, Ramon Trujillo, Jamie S LeBarron, et al. Particle-verification for single-particle, reference-based reconstruction using multivariate data analysis and classification[J]. Journal of Structural Biology, 2008, 164(1): 41-48
[56]Chen J Z, Grigorieff N. SIGNATURE: a single-particle selection system for molecular electron microscopy. J. Struct. Biol[J]. 2007, 157 (1): 168-173
[57]Yuanxin Zhu, Bridge Carragher, David J. Kriegman, et al. Automated identification of filaments in cryoelectron microscopy images[J]. J. Structure Biology, 2001, 135: 302-312
[58]Zeyun Yu, Chandraijit Bajaj. Detecting circular and rectangular particles based on geometric feature detection in electron micrographs[J]. J. Structure Biology, 2004, 145: 168-180
[59]Zhu Y, Carragher B, Mouche F, et al. Automatic particle detection through efficient hough transforms[J]. IEEE Transactions on Medical Imaging, 2003, 22(9): 1053-1062
[60]Boier Martin I M, Marinescu D C, Lynch R E, et al. Identification of spherical virus particles in digitized images of entire electron micrographs[J]. J. Struct. Biol.1997,120: 146-157
[61]Kivioja T, Ravantti J, Verkhovsky A, et al. Local average intensity-based method for identifying spherical particles in electron micrographs[J]. J. Struct. Biol., 2000, 131: 126-134
[62]Lata K R, Penczek P, Frank J. Automatic particle picking from electron micrographs[J]. Ultramicroscopy, 1995, 58: 381-391
[63]Richard J. Hall, Ardan Patwardhan. A two step approach for semi-automated particle selection from low contrast cryo-electron micrographs[J]. J. Structure Biology, 2004, 145: 19-28
[64]Mallick S. P., Yuanxin Zhu, David Kriegman. Detecting particles in cryo-EM micrographs using learned features[J]. J. structure Biology, 2004, 145: 52-62
[65]Niels Volkmann. An approach to automated particle picking from electron micrographs based on reduced representation templates[J]. J. Structure Biology, 2004, 145: 152-156
[66]CO Sorzano, E Recarte, M Alcorlo, et al. Automatic particle selection from electron micrographs using machine learning techniques[J]. Journal of Structural Biology, 2009, 167(3):252-260
[67]Toshihiko Ogura, Chikara Sato. An automatic particle pickup method using a neural network applicable to low-contrast electron micrographs[J]. J. Structure Biology, 2001, 136: 227-238
[68]Toshihiko Ogura, Chikara Sato. Automatic particle pickup method using a neural network has high accuracy by applying an initial weight derived from eigenimages: a new reference free method for single-particle analysis[J]. J. Structure Biology, 2004, 145: 63-75
[69]A. pascual-Montano, L E Donate, M Valle, M. Bàrcena, et al. A novel neural network technique for analysis and classification of EM single-particle images[J]. J. Structure Biology, 2001, 133: 233-245
[70]Toshihiko Ogura, Chikara Sato. Auto-accumulation method using simulated annealing enables fully automatic particle pickup completely free from a matching template or learned data[J]. J. Structure Biology, 2004, 146: 344-358
[71]J R Plaisier, R I Koning, H K Koerten, et al. TYSON: Robust searching, sorting, and selecting of single particles in electron micrographs[J]. J. Structure Biology, 2004, 145: 76-83
[72]B.K. Rath, J. Frank. Fast automatic particle picking from cryo-electron micrographs using a locally normalized cross-correlation function: a case study[J]. J. Structure Biology, 2004, 145: 84-90
[73]N R Voss, C K Yoshioka, M Radermacher, et al. DoG Picker and TiltPicker: Software tools to facilitate particle selection in single particle electron microscopy[J]. Journal of Structural Biology, 2009, 166(2): 205-213
[74]David Woolford, Geoffery Ericksson, Rosalba Rothnagel, et al. SwarmPS: Rapid, semi-automated single particle selection software[J]. Journal of Structural Biology, 2007, 157(1): 174-188
[75]J.R. Plaisier, L. Jiang, J.P. Abrahams,Cyclops: New modular software suite for cryo-EM[J]. Journal of Structural Biology, 2007, 157(1): 19-27
[76]彭士纯.基于多尺度分析的医学图像配准与融合方法研究[D].湖北:华中科技大学博士学位论文, 2006
[77]Qingxiong S Zheng, Michael B Braunfeld, John W Sedat, et al. An improved strategy for automated electron microscopic tomography[J] Journal of Structural Biology, 2004,147: 91–101
[78]David N Mastronarde. Automated electron microscope tomography using robust prediction of specimen movements[J]. Journal of Structural Biology, 2005, 152: 36-51
[79]Frank J, McEwen B F. Alignment by cross-correlation[A]. Electron Tomography: Three Dimensional Imaging with the Transmission Electron Microscope[C]. Plenum, New York, 1992, 8: 205–213
[80]Yu Liu, Pawel A Penczek, Bruce F McEwen, et al. A marker-free alignment method for electron tomography[J]. Ultramicroscopy, 1995, 58: 393-402
[81]Hanspeter Winkler, Kenneth A Taylor. Accurate marker-free alignment with simultaneous geometry determination and reconstruction of tilt series in electron tomography[J]. Ultramicroscopy, 2006, 106: 24-254
[82]Cameron H Owen, Willian JLandis. Alignment of electron tomographic bycorrelation without the use of gold particles[J]. Ultramicroscopy, 1996, 63: 27-38
[83]Sami Brandt, Jukka Heikkonen, Peter Engelhant. Automatic Alignment of Transmission Electron Microscope Tilt Series Without Fiducial Markers[J]. Journal of Structural Biology, 2001, 136: 201-213
[84]Díez DC, Al-Amoudi A, Glynn A-M, et al. Fiducial-less Alignment of Cryo-Sections[J]. Journal of Structural Biology, 2007, 159: 413-423
[85]Sami Brandt, Jukka Heikkonen, Peter Engelhant. Multiphase Method for Automatic Alignment of Transmission Electron Microscope Images Using Markers[J]. Journal of Structural Biology, 2001, 133: 10-22
[86]P Tchelidze, C Sauvage, N Bonnet, L Kilian, et al. Electron tomography of amplified nanogold immunolabelling: Improvement of quality based on alignment of projections with sonograms and use of post-reconstruction deconvolution[J]. Journal of Structural Biology, 2006, 156: 421-431
[87]Penczek P, Marko M, Buttle K, et al. Double-tilt electron tomography[J]. Ultramicroscopy, 1995, 60: 393-410
[88]Díez D C, Anja Seybert, Achilleas S Frangakis. Tilt-series and electron microscope alignment for the correction of the non-perpendicularity of beam and tilt-axis[J]. Journal of Structural Biology, 2006, 154: 195-205
[89]Ress D, Harlow M L, Schwarz M, et al. Automatic acquisition of fiducial markers and alignment of images in tilt series for electron tomography[J]. J. Electron Microsc. 1999, 48(3): 277-287
[90]Benveniste A, Nikoukhah R, willsky A S. Multiscale system theory[A]. Proc.29th IEEE conf. on Decision and Control[C]. IEEE :Honolulu, 1990: 2484-2487
[91]Willsky A S. Multiresolution Markov models for signal and image processing[J]. IEEE Trans., 2002, 90(8): 1396-1458
[92]Benveniste A, Nikoukhah R, willsky A S. Multiscale System Theory[J]. IEEE Trans. Fundamental Theory and Applications, 1994, 14(1): 2-14
[93]Ge Ji, Wang Yaonan, Zhou Bowen, et al. Multi-Scale Shaft Parts Image Edge Detection and Measurement Based on Wavelet Transform[A]. Proceedings of the 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and IndustrialApplication[C], 2008: 371-375
[94]Pennec E L, Mallat S. Sparse geometric image representation with bandelets[J]. IEEE Trans. Image Processing, 2005, 14(4): 423-438
[95]Qiming L, Khoshgoftaar T M. Unsupervised multiscale color image segmentation based on MDL principle[J]. IEEE Trans. Image Processing, 2006, 15(9): 2755-2761
[96]李军宏,潘泉,陈玉春等.基于Radon变换的纹理图像多尺度不变量分析算法[J].中国图象图形学报, 2005, 10(9): 1117-1123
[97]焦李成,孙强.多尺度变换域图像的感知与识别:进展和展望[J].计算机学报, 2006, 29(2): 177-193
[98]Witkin A P. Scale space filtering: A new approach to multiscale description[A]. Proc. Image Understanding[C], 1984, 1(1): 79-95
[99]Tomasi C, Manduchi R. Bilateral filtering for gray and color images [A]. In: Proceedings of the Sixth International Conference on Computer Vision [C]. Washington, DC, USA: IEEE Computer Society, 1998: 839-846
[100]Nguyen D V, Recke D M. Tumor classification by partial least squares using microarray gene expression data[J], Bioinformatics, 2002, 13(1): 39-50
[101]Jangsun Baekaf, Minsoo Kim. Face recognition using partial least squares components[J]. Pattern Recognition, 2004, 37(6): 1303-1306
[102]Jie Song, Jiaxing Cheng, Zhongwei Xu. Face Recognition Based on Covering Algorithm and PLS[A]. Proceedings of the 6th World Congress on Intelligent Controland Automation[C], Dalian, 2006: 21-23
[103]楼安平,杨新.基于偏最小二乘法与支持向量机的人脸识别[J].计算机仿真, 2005, 22(12): 166-168
[104]孙宁,冀贞海,邹采荣,等.基于2维偏最小二乘法的图像局部特征提取及其在面部表情识别中的应用[J].中国图象图形学报, 2007, 12(5): 847-853
[105]王惠文.偏最小二乘回归方法及其应用[M].北京:国防工业出版社, 1999
[106]Wold S, Martens H, Wold H. The Multivariate Calibration Problem in Chemistry Solved by the PLS method[A]. Proceedings of Conference on Matrix Pencils,Lecture Notes in mathematics[C], Springer, Heidelberg,1983,286-293
[107]Wold S, Trygg J, Berglund A, et al. Some recent developments in PLSmodeling[J]. Chemomertics and lntelligent Laboratory Systems, 2001, 58(1):131-150
[108]H?skuldsson A. PLS Regression Methods[J]. Journal of Chemometrics, 1988, 2:211-228
[109]H?skuldsson A. Causal and path modeling[J]. Chemometrics and Intelligent Laboratory Systems, 2001, 58:287-311
[110]孙权森.基于相关投影分析的特征抽取与图像识别研究[D].南京:南京理工大学, 2006
[111]Baek J, Kim M. Face recognition using partial least squares components[J]. Pattern recognition, 2004, 37(6): 1303-1306
[112]Noeairi H, Mostafa Q E, Vigneau E, et al. Discrimination on latent components wih respect to patterns: Application to multicollinear data[J]. Computational Statistics & Data Analysis, 2005, 48:139-147
[113]Rosipal R, Trejo L J, Matthews B. Kenrel PLS-SVC for Linear and Nonlinear Classification[A]. Porceedings of the Twentieth International Conference on Machine Learning(ICML)[C], Washington DC, 2003: 640-647
[114]Rosipal R. Kernel Partial Least Squares for Nonlinear Regression and Discrimination[J]. Neural Network World, 2003, 13(3):291-300
[115]C-M Pun, M-C Lee. Log-polar wavelet energy signatures for rotation and scale invariant texture classification[J]. IEEE Trans. Pattern Anal. Mach. Intell., 2003, 25(5): 590-603
[116] Peiling Cui, Junhong Li, Quan Pan,et al. Rotation and scaling invariant texture classification based on Radon transform and multiscale analysis[J]. Pattern Recognition Letters, 2006, 27(5): 408-413
[117]K Jafari-Khouzani, H Soltanian-Zadeh. Rotation Invariant Multiresolution Texture Analysis Using Radon and Wavelet Transforms[J]. IEEE Tran on Image Process, 2005, 14(6): 783-794
[118]Pizer S M, Koenderink J J, Lifshitz L M, et al. An image description for object definition based on external regions in the stack[J]. Proc. of 9th Conf. Information Processing in Medical Imaging. Boston, MA: Martinus Nijhoff, 1986
[119]M K Hu,Visual pattern recognition by moment invariants[J]. IEEE Transactionson Information Theory, 1962, 8: 179-182
[120]卢振泰,冯衍秋,冯前进,等.基于等效子午面与互信息量的医学图像配准[J].计算机学报, 2009, 32(8): 1611-1617
[121]杨金宝.基于灰度相似性测度的医学图像配技术研究[D].山东:山东大学, 2008
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |