基于主成分分析与迭代最近点的三维膝关节配准

doi:10.13190/j.jbupt.2019-165

北京邮电大学学报 ›› 2020, Vol. 43 ›› Issue (3): 112-117.doi: 10.13190/j.jbupt.2019-165

基于主成分分析与迭代最近点的三维膝关节配准

王小玉, 陈琳

哈尔滨理工大学计算机科学技术学院, 哈尔滨 150080

收稿日期:2019-08-13 出版日期:2020-06-28 发布日期:2020-06-24
作者简介:王小玉(1971-),女,教授,E-mail:wngxiaoyu@hrbust.edu.cn.
基金资助:
国家自然科学基金项目（60572153，60972127）；黑龙江省教育厅科学技术项目（12541177）

Three-Dimensional Knee Joint Registration Based on Principal Component Analysis and Iterative Closest Point

WANG Xiao-yu, CHEN Lin

School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China

Received:2019-08-13 Online:2020-06-28 Published:2020-06-24
Supported by:

摘要/Abstract

摘要： 针对膝关节与假体空间坐标系不一致的问题，提出了一种基于主成分分析（PCA）与迭代最近点（ICP）算法相结合的三段式配准方法.采用PCA结合两次ICP算法的配准策略，对膝关节与关节假体的点云数据进行PCA配准处理，然后对得到的初始配准结果采用ICP算法进行调整，最后对调整好姿态的点云数据再次进行ICP配准，从而将其空间坐标轴调整到一致.实验结果表明，三段式配准方法相较于其他算法，可在保持较高配准精度的同时缩短配准时间.

关键词: 点云配准, 迭代最近点, 主成分分析, 膝关节

Abstract: In view of inconsistency between knee joint and prosthesis in spatial coordinate system, a three-stage registration method based on principal component analysis (PCA) and iterative closest point (ICP) algorithm is proposed, which adopts PCA and ICP twice. Firstly, the point cloud data of knee joint and joint prosthesis is registered by PCA. then ICP algorithm is used to adjust the initial registration results. Finally, ICP registration is carried out again for the adjusted point cloud data, so as to adjust its spatial coordinate axis to be consistent. Experiments show that compared with other algorithms, the three-stage registration method can keep high registration accuracy and shorten the registration time.

Key words: opint cloud registration, iterative closest point, principal component analysis, knee joint

中图分类号:

TN911.22

王小玉, 陈琳. 基于主成分分析与迭代最近点的三维膝关节配准[J]. 北京邮电大学学报, 2020, 43(3): 112-117.

WANG Xiao-yu, CHEN Lin. Three-Dimensional Knee Joint Registration Based on Principal Component Analysis and Iterative Closest Point[J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(3): 112-117.

参考文献

[1] Besl P J, McKay N D. A method for registration of 3D shapes[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(2):239-256.
[2] Chen H, Bhanu B. Contour matching for 3D ear recognition[C]//Proceedings of IEEE Workshop on Application of Computer Vision. California:University of California 2005:123-128.
[3] 盖宇. 基于两步式迭代最近点的三维人耳配准算法[J]. 微型机与应用, 2015, 34(15):22-25. Gai Yu. 3D human ear registration algorithm based on two-step iterative closest point[J]. Microcomputer and Applications, 2015, 34(15):22-25.
[4] 杨飚, 李三宝, 王力. 基于正态分布变换与迭代最近点的快速点云配准算法[J]. 科学技术与工程, 2017, 17(15):91-95. Yang Biao, Li Sanbao, Wang Li. Fast point cloud registration algorithm based on NDT and ICP[J]. Science and Technology and Engineering, 2017, 17(15):91-95.
[5] 严剑锋, 邓喀中. 基于PCA和Delaunay剖分点云配准算法研究[J]. 现代测绘, 2016, 39(1):29-32. Yan Jianfeng, Deng Kazhong. Research on point cloud registration algorithm based on PCA and delaunay[J]. Modern Surveying and Mapping, 2016, 39(1):29-32.
[6] 王育坚, 吴明明, 高倩. 基于保局PCA的三维点云配准算法[J]. 光学技术, 2018, 44(5):562-568. Wang Yujian, Wu Mingming, Gao Qian. 3D point cloud registration algorithm based on locality preserving PCA[J]. Optical Technology, 2018, 44(5):562-568.
[7] 熊风光. 三维点云配准技术研究[D]. 太原:中北大学, 2018.
[8] 赵夫群, 贾一婷. 基于几何属性和改进ICP的点云配准方法[J]. 信息技术, 2019, 43(4):33-38. Zhao Fuqun, Jia Yiting. Point cloud registration method based on geometric attributes and improved ICP[J]. Information Technology, 2019, 43(4):33-38.
[9] 翁飞, 侯文广, 曾明平. 用于三维医学图像配准的"粗精"混合算法研究[J]. 北京生物医学工程, 2016, 35(1):12-17. Weng Fei, Hou Wenguang, Zeng Mingping. A ‘coarse and fine’ hybrid algorithm for three-dimensional medical image registration[J]. Beijing Biomedical Engineering, 2016, 35(1):12-1.

基于主成分分析与迭代最近点的三维膝关节配准

Three-Dimensional Knee Joint Registration Based on Principal Component Analysis and Iterative Closest Point

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