弹性图像配准方法在气象图像中的应用
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摘要
图像配准是计算机视觉和模式识别领域中的一项重要课题，在遥感探测、医学成像和基于多传感器融合的目标识别等研究中都有重大的应用价值。气象图像配准是为了实现一幅图像与另一幅图像上对应点、面通过几何变换达到空间上的一致，从而将多种模态的图像信息融合成一幅新的图像，消除像差对多模态图像信息融合所产生的影响。
本文针对气象卫星分时成像所产生的同时具有刚性形变和弹性形变的图像序列，深入系统的研究了图像刚性配准和弹性配准的方法，并根据气象云图的特性和项目的要求对算法进行了一些改进。
1．根据气象云图刚性形变和弹性形变同时存在的特点，采用分级变换模型进行图像配准，即图像整体的旋转和平移等刚性形变用仿射变换模型来表示，而云的局部扭曲等弹性形变用基于B样条的FFD模型来表示。首先利用基于特征点的刚性配准算法进行图像整体的配准，校正图像的刚性形变，使图像整体趋于一致；再利用基于B样条的弹性配准算法进行图像局部的弹性配准，校正图像的弹性形变，使两幅图像完全配准。实验结果证明，采用分级变换模型不但提高了图像配准的精度，还提高了图像配准的运行速度。
2．改进了刚性配准算法中特征点匹配的距离约束算法，在距离约束算法中添加了自适应阈值调整方法。较之人为确定阈值的算法，改进的自适应阈值调整的距离约束算法更具有通用性，可以针对不同类型的图像自动进行阈值调整，更好的剔除伪匹配点对。
3．改进了弹性配准算法中的图像更新策略，利用贪婪算法对控制点和图像进行局部更新。改进的利用贪婪算法的局部更新策略在对控制点遍历的同时便逐步更新控制点的位置信息以及图像局部的灰度信息，并将更新后的控制点位置信息和图像灰度信息带入下一步的计算，用于其他控制点网格子块的更新。对于一般的实验图像，利用贪婪算法的局部更新策略只需从图像四个角对控制点分别遍历一次，便可较好的实现整幅图像的弹性配准。较之原先的图像整体更新策略，改进后算法的执行速度显著的提高。
利用本文的算法，对同时存在整体的刚性形变和局部的弹性形变的图像进行了一系列的图像配准实验，实验结果证明本文算法具有一定的可实施性。
Image registration is a hot topic in computer vision and pattern recognition, it has great values in medical imaging and multimodal image fusion. Image registration which can map points (or surface) from one image to homologous points on an objects in the second images is one of the fundamental tasks in image processing, and then fusion them into a new image.
In this paper, in allusion to the imaging of the tiros which produce rigid deformation and nonrigid deformation, we review different methods in image registration, include rigid and nonrigid registration, and propose a few amelioration as followed.
1. Due to the characteristic of the weather image, a hierarchical transformation model of the motion of the image has been introduced. The global motion of the image is modeled by an affine transformation which use the rigid registration based on Forstner interesting operator, while the local image motion is described by a free-form deformation (FFD) based on B-splines.
2. In the rigid registration process, we improve on the distance restrict algorithm. A suitable threshold adjust method is add to the conventional algorithm. Compare with the conventional distance restrict algorithm, the improving method is more all-purpose, and can better removing the fake matching points.
3. In the nonrigid registration process, we propose a new strategy to updating the control point grid (CPG) and the grid area intensity, which update the CPG and local area intensity step by step,and the result is using in the next computing. Experiments prove that the new strategy decrease the runtime of the nonrigid registration visible.
In this paper, the algorithm has been applied to the fully automated registration of weather images, the result clearly indicate that the approach we proposed can well recover the motion and deformation of the weather images.
本文针对气象卫星分时成像所产生的同时具有刚性形变和弹性形变的图像序列，深入系统的研究了图像刚性配准和弹性配准的方法，并根据气象云图的特性和项目的要求对算法进行了一些改进。
1．根据气象云图刚性形变和弹性形变同时存在的特点，采用分级变换模型进行图像配准，即图像整体的旋转和平移等刚性形变用仿射变换模型来表示，而云的局部扭曲等弹性形变用基于B样条的FFD模型来表示。首先利用基于特征点的刚性配准算法进行图像整体的配准，校正图像的刚性形变，使图像整体趋于一致；再利用基于B样条的弹性配准算法进行图像局部的弹性配准，校正图像的弹性形变，使两幅图像完全配准。实验结果证明，采用分级变换模型不但提高了图像配准的精度，还提高了图像配准的运行速度。
2．改进了刚性配准算法中特征点匹配的距离约束算法，在距离约束算法中添加了自适应阈值调整方法。较之人为确定阈值的算法，改进的自适应阈值调整的距离约束算法更具有通用性，可以针对不同类型的图像自动进行阈值调整，更好的剔除伪匹配点对。
3．改进了弹性配准算法中的图像更新策略，利用贪婪算法对控制点和图像进行局部更新。改进的利用贪婪算法的局部更新策略在对控制点遍历的同时便逐步更新控制点的位置信息以及图像局部的灰度信息，并将更新后的控制点位置信息和图像灰度信息带入下一步的计算，用于其他控制点网格子块的更新。对于一般的实验图像，利用贪婪算法的局部更新策略只需从图像四个角对控制点分别遍历一次，便可较好的实现整幅图像的弹性配准。较之原先的图像整体更新策略，改进后算法的执行速度显著的提高。
利用本文的算法，对同时存在整体的刚性形变和局部的弹性形变的图像进行了一系列的图像配准实验，实验结果证明本文算法具有一定的可实施性。
Image registration is a hot topic in computer vision and pattern recognition, it has great values in medical imaging and multimodal image fusion. Image registration which can map points (or surface) from one image to homologous points on an objects in the second images is one of the fundamental tasks in image processing, and then fusion them into a new image.
In this paper, in allusion to the imaging of the tiros which produce rigid deformation and nonrigid deformation, we review different methods in image registration, include rigid and nonrigid registration, and propose a few amelioration as followed.
1. Due to the characteristic of the weather image, a hierarchical transformation model of the motion of the image has been introduced. The global motion of the image is modeled by an affine transformation which use the rigid registration based on Forstner interesting operator, while the local image motion is described by a free-form deformation (FFD) based on B-splines.
2. In the rigid registration process, we improve on the distance restrict algorithm. A suitable threshold adjust method is add to the conventional algorithm. Compare with the conventional distance restrict algorithm, the improving method is more all-purpose, and can better removing the fake matching points.
3. In the nonrigid registration process, we propose a new strategy to updating the control point grid (CPG) and the grid area intensity, which update the CPG and local area intensity step by step,and the result is using in the next computing. Experiments prove that the new strategy decrease the runtime of the nonrigid registration visible.
In this paper, the algorithm has been applied to the fully automated registration of weather images, the result clearly indicate that the approach we proposed can well recover the motion and deformation of the weather images.
引文
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2. Benoit M Dawant. Non_rigid registration of medical images: purpose and method, a short survey. IEEE 2002
3. Karl Rohr. Elastic registration of Multimodal Medical Images: A Survey. http://www.kuentliche-intelligenz.de, 2000
4. Christensen G, Rabbitt R D, Miller et al. Deformable templates using large deformation kinematics[J]. IEEE Trans Image Process. 1996, 5:1435-1447
5. Touraille Eric, Boire Jean. Elastic registration of MRI scans using Fast DCT. Proceedings of the 22th, Annual EMBS Iternational Conference. 2000
6. Christensen G, Joshi S, Miller M. Volumetric transformation of brain anatomy[J]. IEEE Trans Med Imaging, 1997, 16:864-877
7. Karl Rohr, Stiehl HS, Sprengel R, et al. Landmark-Based Elastic Registration Using Approximating Thin-plate Splines. IEEE Trans Med Imag, 2001, 20(6):526-534
8. Bro-Nielsen M, Gramkow C. Fast fluid Registration of medical images. SPIE Med Imaging. 1996:267-276
9. Hellier P, Barillot Ch. Cooperation between local and global approaches to register brain images. Int Corf On Information Processing in Medical Imaging, IPM01.
10. Edwards P, Hill D, Little J, and Hawkes D. A threecomponent deformation model for image-guided surgery. Med. Image Anal. 1998, Vol 2
11. Joseph Hajnal, David Hawkes, Derek Hill. Medical image registration. http://www-ipg.umds.ac.uk/d.hill/hhh/2001
12. Frederik Maes, Dirk Vandermeulen, Paul Suetens. Medical image registration using mutual information[J]. Proceedings of IEEE. 2003:91(10):1699-1722
13. Lester H, Arridge S. A survey of hierarchical non-linear medical image registration[J]. Pattern Recognition. 1999:32(1);129-149
14. 陈昱，庄天戈，王合．直方图估计互信息在非刚性图像配准中的应用．计算机学报．2000，23(2)：444-447
15. 冯林等．用分层互信息和薄板样条实现医学图像弹性自动配准．计算机辅助设计与图形学报．2005，17(7)：1492-1496
16. Maintz J and Viergever M. A survey of medical image registration. Med. Image Anal. 1998, vol.2
17. 何元烈．多模医学图像配准与融合技术及医学智能辅助诊断系统研究．[博士学位论文]．广东，华南理工大学．2006
18. 何斌，马天予，王运坚，朱红莲．Visual C++数字图像处理．第二版．人民邮电出版社，2002
19. 王海南，郝重阳，雷方元，张先勇．非刚性医学图像配准研究综述．计算机工程与应用．2005．11：180-184
20. Rueckert D, Sonoda L, Hayes C et al. Nonrigid registration using free-form deformations: application to breast MR image[J]. IEEE Trans on Medical Imaging. 1999,18(8):712-721
21. Kybic J, Unser M. Fast parametric elastic image registration[J]. IEEE Trans on Image Processing. 2003, 12(11):1427-1442
22. Bajcsy R et al. Multiresolution elastic matching[J]. Computer vision, Graphics and Image Processing. 1989, 46:1-21
23. 郑明玲，刘衡竹．遥感图像配准中特征点选择的高性能算法研究及其实现．计算机学报．2004，第27卷第9期
24. 胡明昊，任明武，杨静宇．一种快速实用的特征点匹配算法．计算机工程．2004，第30卷第9期
25. Arce G, Gallagher N. State description for the root signal set of median filters[J]. IEEE Trans A coust, Speech Signal Processing. 1982, 30:894-902
26. Lee Y, Kassam S. Generalized median filtering and related nonlinear filtering techniques[J]. IEEE Trans A coust, Speech, Signal Processing. 1985
27. Bovik A, Huang T, Munson D. A generalization of median filters using linear combination of order statistics[J]. IEEE Trans A coust, Speech, Signal Processing. 1983, 31(6):1342-1350
28. Bemstein R. Adaptive Nonlinear filters for simultaneous removal of different kinds of noise in image[J]. IEEE Trans Circuits and Systems. 1987, 34(11)
29. Peli T, Lim J. Adaptive Filtering for Image Enhancement[J]. Optical Eng. 1982, 21(1):108-112
30. 王建华，王春平，贾洪涛．自适应中值滤波器在图像降噪技术中的应用．测控技术．2004，第23卷第5期
31. Collignon A, Maes F, et al. Automated multimodality image registration using information theory. Information Processing Medical Imaging: proc, 14th Int, Conf. 1995, pp. 263-274
32. Viola P. Alignment by maximization of mutual information. Ph.D dissertation. Massachusetts Inst, Technology, Cambridge. 1995
33. Studholme C, Hill D, and Hawkes D. An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognit. 1998, vol.32 no.1
34. Studholme C, Hill D, and Hawkes D. Automated 3D registration of MR and PET brain images by multi-resolution optimization of voxel similarity measure. Med. Phys. 1997, vol.24 no.1
35. Studholme C, Hill D, and Hawkes D. Automated 3D registration of MR and CT image of the head. Med. Image Anals. 1996, vol.1 no.2
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