一种多模态医学影像鲁棒配准方法

doi:10.3969/j.issn.1001-2400.2011.06.005

J4 ›› 2011, Vol. 38 ›› Issue (6): 30-36+145.doi: 10.3969/j.issn.1001-2400.2011.06.005

一种多模态医学影像鲁棒配准方法

李琦;姬红兵;同鸣

(西安电子科技大学电子工程学院，陕西西安 710071)

收稿日期:2010-08-17 出版日期:2011-12-20 发布日期:2011-11-29
通讯作者: 李琦
作者简介:李琦(1979-)，女，讲师，西安电子科技大学博士研究生，E-mail: qili@xidian.edu.cn
基金资助:
陕西省自然科学基金资助项目(SJ08F15)

Robust registration of multimodality medical images based on the principal ordinal feature and hybrid entropy

LI Qi;JI Hongbing;TONG Ming

(School of Electronic Engineering, Xidian Univ., Xi'an 710071， China)

Received:2010-08-17 Online:2011-12-20 Published:2011-11-29
Contact: LI Qi

摘要/Abstract

摘要：

为提高多模态医学影像配准的鲁棒性和精度，提出了一种基于主定序和混合熵的配准新方法．首先利用主成分分析方法定义了图像的主定序特征，描述图像邻域像素间的空间信息和微观结构特性;进而结合图像灰度信息构造了一种基于混合熵的配准测度，有效保证了配准测度函数的光滑性和收敛性．多组多模态医学影像测试结果表明，新方法能有效抑制噪声，具有很高的配准精度，鲁棒性强，优于现有的几种方法．

关键词: 图像配准, 互信息, 主定序特征, 混合熵

Abstract:

To improve the robustness and precision of multimodality medical image registration, a principal ordinal feature and hybrid entropy based registration method is presented. A principal ordinal feature is defined and used to represent the spatial information between the neighboring pixels and the properties of the specified micro-structure in medical images. Integrating with pixel intensities, a similarity measure based on hybrid entropy is defined to register multimodality images. The proposed method is demonstrated using several pairs of multimodality medical images and experimental results show that the noise of images can be effectively suppressed and that compared with some existing methods, the proposed registration algorithm is of higher precision and better robustness.

Key words: image registration, mutual information, principal ordinal feature, hybrid entropy

中图分类号:

TP391.4

李琦;姬红兵;同鸣. 一种多模态医学影像鲁棒配准方法[J]. J4, 2011, 38(6): 30-36+145.

LI Qi;JI Hongbing;TONG Ming. Robust registration of multimodality medical images based on the principal ordinal feature and hybrid entropy[J]. J4, 2011, 38(6): 30-36+145.

参考文献

［1］那彦, 杨万海,李勇朝. 图像信息融合与医学图像综合显示［J］. 西安电子科技大学学报, 2004, 31(1): 21-24.
Na Yan, Yang Wanhai, Li Yongzhao. Image Information Fusion and Sunthesis Display of Medical Images［J］. Journal of Xidian University, 2004, 31(1): 21-24.
［2］ Gholipour A, Kehtarnavaz N, Briggs R, et al. Brain Functional Localization: a Survey of Image Registration Techniques［J］. IEEE Trans on Medical Imaging, 2007, 26(4): 427-451.
［3］ Loeckx D, Slagmolen P, Maes F, et al. Nonrigid Image Registration Using Conditional Mutual Information［J］. IEEE Trans on Medical Imaging, 2010, 29(1): 19-29.
［4］ Rueckert D, Clarkson M J, Hill D L G, et al. Non-rigid Registration Using Higher-order Mutual Information［C］//Society of Photo-Optical Instrumentation Engineers, Proc SPIE Medical Imaging 2000: Image Processing. San Diego: SPIE, 2000: 438-447.
［5］ Chen B J, Li J L, Chen G. Study of Medical Image Registration Based on Second-Order Mutual Information［C］//Proc of the 1st International Conference on Bioinformatics and Biomedical Engineering. Wuhan: ICBBE, 2007: 956-959.
［6］ Pluim J P W, Maintz J B A, Viergever M A. Image Registration by Maximization of Combined Mutual Information and Gradient Information［J］. IEEE Trans on Medical Imaging, 2000, 19(8): 809-814.
［7］ Tan T N, Sun Z N. Ordinal Representations for Biometrics Recognition ［C］//EUSIPCO. Poznan: EURASIP, 2007: 35-39.
［8］ Han Y F, Tan T N, Sun Z N, et al. Embedded Palmprint Recognition System on Mobile Devices［C］//ICB 2007, LNCS 4642. Berlin: Springer-Verlag, 2007: 1184-1193.
［9］高莉, 黄力宇, 丁翠玲. 结合PCA和ICA的脑磁信号消噪研究［J］. 西安电子科技大学学报, 2007, 34(6): 939-943.
Gao Li, Huang Liyu, Ding Cuiling. Study of Removal of Artifacts in MEG Using PCA and ICA［J］. Journal of Xidian University, 2007, 34(6): 939-943.
［10］ Valle Y D, Venayagamoorthy G K, Mohagheghi S, et al. Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems［J］. IEEE Trans on Evolutionary Computation, 2008, 12(2): 171-195.
［11］胡顺波, 刘常春. 医学图像配准中的数据抽样方法研究［J］. 光电子激光, 2008, 19(2): 237-241.
Hu Shunbo, Liu Changchun. Data Subsampling in Medical Image Registration［J］. Journal of Optoelectronics Laser, 2008, 19(2): 237-241.
［12］ Johnson K A. The Whole Brain Atlas［DB/OL］. ［2010-05-17］. http://www.med.harvard.edu/AANLIB/home.html.
［13］ McConnel Brain Imaging Center Montreal Neurological Institute, McGill Univ. Brain Web［DB/OL］. ［2010-05-17］. http://mouldy.bic.mni.mcgill.ca/brainweb.
［14］ Xu M, Chen H, Varshney P K. Ziv-Zakai Bounds on Image Registration［J］. IEEE Trans on Signal Processing, 2009, 57(5): 1745-1755.
［15］ Nikolov S. Image Fusion.org［DB/OL］. ［2010-05-17］. http://www.imagefusion.org/.

[1]	黄樟钦,母召,岑陈,高寒. 一种多路视频的360度实时拼接技术[J]. 西安电子科技大学学报, 2020, 47(2): 1-8.
[2]	王红艳,朱利民,张潘杰,李金屏. 一种利用外轮廓配准的活塞侧面缺陷检测方法[J]. 西安电子科技大学学报, 2019, 46(5): 75-83.
[3]	肖利军,郭继昌,顾翔元. 一种采用冗余性动态权重的特征选择算法[J]. 西安电子科技大学学报, 2019, 46(5): 155-161.
[4]	莫建文,徐凯亮,林乐平,欧阳宁. 结合互信息最大化的文本到图像生成方法[J]. 西安电子科技大学学报, 2019, 46(5): 180-188.
[5]	张金强;索志勇;李真芳;保铮. 地形高程自适应的星载InSAR图像配准方法[J]. 西安电子科技大学学报, 2018, 45(2): 31-36.
[6]	王玉玺;黄国策;李伟;王叶群. 杂波和有色噪声条件下MIMO雷达波形设计[J]. 西安电子科技大学学报, 2017, 44(4): 125-131.
[7]	路文;李帅;徐红强;何立火. 一种利用像素局部相关性的图像质量评价方法[J]. 西安电子科技大学学报, 2017, 44(1): 94-99.
[8]	靳峰;冯大政. 一种快速准确的图像配准算法[J]. J4, 2015, 42(6): 88-93.
[9]	夏冰冰;赵险峰;张弘 . 拟合盲隐写分析结果的隐写组合测评方法[J]. J4, 2015, 42(4): 153-158.
[10]	吴晨阳;牛海军;迟旭燕. 一种新的特征点均匀化多光谱图像配准方法[J]. J4, 2013, 40(4): 56-59.
[11]	杨贞萍;牛海军;丁磊. 一种新的NSCT超分辨率图像复原技术[J]. J4, 2012, 39(1): 34-37+85.
[12]	纠博;刘宏伟;李丽亚;吴顺君. 雷达波形优化的特征互信息方法 [J]. J4, 2009, 36(1): 139-144.
[13]	纠博;刘宏伟;李丽亚;吴顺君. 一种基于互信息的波形优化设计方法 [J]. J4, 2008, 35(4): 678-684.
[14]	徐青1;廖桂生1;刘颖1;梁璟2. 一种分布式小卫星立体阵基线误差估计方法 [J]. J4, 2008, 35(4): 668-672.
[15]	赖睿(1;2);刘上乾(2);杨银堂(1);李跃进(1). 一种基于LTS-HD测度的低信噪比图像鲁棒配准方法 [J]. J4, 2006, 33(6): 902-906.

一种多模态医学影像鲁棒配准方法

Robust registration of multimodality medical images based on the principal ordinal feature and hybrid entropy

PDF (PC)

赞

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 10