Medical image registration algorithm based on Powell algorithm and improved genetic algorithm

doi:10.3724/SP.J.1087.2013.00640

Journal of Computer Applications ›› 2013, Vol. 33 ›› Issue (03): 640-644.DOI: 10.3724/SP.J.1087.2013.00640

• Network and distributed techno • Previous Articles Next Articles

Medical image registration algorithm based on Powell algorithm and improved genetic algorithm

LI Chao^1*, LI Guangyao¹, TAN Yunlan^2,1, XU Xianglong¹

1. College of Electronics and Information, Tongji University, Shanghai 201804, China;
2. College of Electronics and Information Engineering, Jinggangshan University, Jinggangshan Jiangxi 343009, China

Received:2012-09-14 Revised:2012-12-14 Online:2013-03-01 Published:2013-03-01
Contact: Chao LI

基于Powell算法与改进遗传算法的医学图像配准方法

李超^1*,李光耀¹,谭云兰^2,1,徐祥龙¹

1.同济大学电子与信息工程学院,上海 201804;
2.井冈山大学电子与信息工程学院,江西井冈山 343009

通讯作者: 李超
作者简介:李超(1979-),男,安徽合肥人,讲师,博士研究生,主要研究方向:虚拟现实、图形图像处理; 李光耀(1965-),男,安徽安庆人,教授,博士生导师,主要研究方向:大规模城市建模与仿真、图形图像处理; 谭云兰(1972-),女,江西新干人,副教授,博士研究生,主要研究方向:图像处理; 徐祥龙(1988-),男,山东临沂人,硕士研究生,主要研究方向:地形建模与仿真、图形图像处理。
基金资助:
国家863计划项目(2010AA122200)。

Abstract

Abstract: Concerning the faults of local extremum in image registration based on mutual information, a new medical image registration method based on Powell and improved genetic algorithm was proposed in this paper. It put forward an improved method regarding the shortcomings of the standard genetic algorithm, such as slow convergence and prematurity that will result in artifacts, and generated the iteration individual by Logistic chaos map. This method utilized the multi-resolution analysis strategy and searched for the optimal of the objective function by this hybrid optimized algorithm in the lowest resolution image level. Then it continued the optimization course and accomplished the image registration by this optimal data with the Powell algorithm. The experimental results indicate that this algorithm can effectively improve the image registration velocity and avoid local extremum of the operator while getting better performance of image precision in contrast to the Powell algorithm and unimproved genetic algorithm.

Key words: mutual information, Powell algorithm, improved genetic algorithm, medical image registration, Logistic chaos map

摘要： 针对基于互信息图像配准的局部极值问题,提出一种基于Powell算法与改进遗传算法结合的医学图像配准方法。该方法对标准遗传算法存在的收敛速度慢、易早熟、有可能导致误配的缺陷,提出了相应的改进策略; 采用Logistic混沌映射生成迭代过程中的个体; 运用基于小波变换的多分辨率分析策略,采用混合优化算法在图像的最低分辨率层进行全局优化,以全局最优值,结合Powell算法完成医学图像配准。实验结果表明,所提方法可有效避免优化算子陷入局部极值,并提高了配准速度; 相对于纯Powell方法和未改进的遗传算法,配准的精确度和性能更好。

关键词: 互信息, Powell算法, 改进遗传算法, 医学图像配准, Logistic混沌映射

CLC Number:

TP391

LI Chao LI Guangyao TAN Yunlan XU Xianglong. Medical image registration algorithm based on Powell algorithm and improved genetic algorithm[J]. Journal of Computer Applications, 2013, 33(03): 640-644.

李超李光耀谭云兰徐祥龙. 基于Powell算法与改进遗传算法的医学图像配准方法[J]. 计算机应用, 2013, 33(03): 640-644.

References

[1]郑莹,李光耀. 基于区域互信息和局部频率信息的双向医学图像配准[J]. 中国图象图形学报, 2011,16(1):90-96.
[2]张汗灵,杨帆. 基于互信息和混合优化算法的多模医学图像配准[J].湖南大学学报:自然科学版,2006,33(1):117-120.
[3]杨日容.基于遗传算法及最大互信息的医学图像配准研究[D]. 昆明:昆明理工大学,2005:10-16.
[4]刘斌,彭嘉雄.图像配准的小波分解方法[J].计算机辅助设计与图形学报,2003,15(3): 1070-1073.
[5]PARADLOS P M, SHALOWAY D, XUE G L. Optimization methods for computing global minima of nonconvex potential energy functions[J]. Journal of Global Optimization, 1994, 4(1):17-133.
[6]NELDER J A, MEAD R. A simplex method for function minimization[J]. The Computer Journal, 1965,7(4):308-313.
[7]WILKINSON B, ALLEN M. Parallel programming techniques and applications using networked workstations and parallel computers[M]. 影印版. 北京:机械工业出版社, 2002:45-56.
[8]袁伟,时公涛,蒋咏梅. 一种基于ROEWA算子和GA-Powell算法的SAR图像配准方法[J]. 信号处理, 2009,25(8):190-193.
[9]DING H, BIAN Z H. A sub-pixel registration approach based on powell algorithm[J]. Acta Photonica Sinica, 2009, 38(12):46-49.
[10]刘叶青,刘三阳,谷明涛. Powell算法在线性支持向量机中的应用[J]. 计算机工程,2011,37(12):161-163.
[11]INGBER L. Very fast simulated re-annealing[J]. Mathematical and Computer Modeling, 1989,12(8): 967-973.
[12]ARTS E, KORST J. Simulated annealing and boltzmann machine[M]. New York: John Wiley & Sons, 1989:23-36.
[13]DEKKERS A, ARTS E. Global optimization and simulated annealing[J]. Mathematical Programming, 1991,50(3): 367-393.
[14]江军,於文雪,舒华忠.鲍威尔和模拟退火优化算法结合的多分辨率三维图像配准[J]. 生物医学工程研究, 2004,23(3):175-177.
[15]KENNEDY J, EBERHART R. Particle swarm optimization[C]// Proceedings of IEEE International Conference on Neural Networks. Piscataway, NJ: IEEE Press, 1995,4:1942-1948.
[16]EBERHANT R, KENNEDY J. A new optimizer using particle swarm theory[C]// Proceedings of the 6th International Symposium on Micromachine and Human Science. Piscataway, NJ: IEEE Press, 1995, 10:39-43.
[17]SHI Y, EBERHART R. A modified particle swarm optimizer[C]// IEEE World Congress on Computational Intelligence. Piscataway, NJ: IEEE Press, 1998, 1:69-73.
[18]de JONG K A. Analysis of the behavior of a class of genetic adaptive systems[D]. Ann Arbor, MI: University of Michigan,1975:23-42.
[19]GOLDBERG D E. Genetic algorithms in search,optimization and machine learning[M]. Upper Saddle River: Addison Wesley,1989:67-88.
[20]BACK T. The interaction of mutation rate, selection and self-adaptation within a genetic algorithm[C]// Proceedings of the 2nd Conference on Parallel Problem Solving from Nature. North-Holland: Elsevier, 1992:82-94.
[21]王小平,曹立明.遗传算法——理论、应用与软件实现[M].西安:西安交通大学出版社,2002:37-64.
[22]DAVIS L. Handbook of genetic algorithms[M]. New York:Van Nostrand Reinhold, 1991:21-45.
[23]MENG F L, WU S X. Research of genetic algorithm in function optimizer based on HCI[C]// Proceedings of the IEEE International Symposium on IT in Medicine and Education. Piscataway, NJ: IEEE Press, 2008,1:1049-1052.
[24]MEHMET C, OZSAGIAM M Y. A comparative study on particle swarm optimization and genetic algorithms for traveling salesman problems[J]. Cybernetics and Systems, 2009,40(6):490-507.
[25]NASER Z, JAN K S, AlOTHMAN A K. Real coded genetic algorithm compared to the classical method of fast Fourier transformation harmonics analysis[C]// Proceedings of the 41st International Universities Power Engineering Conference. Piscataway, NJ: IEEE Press, 2006,3:1021-1025.
[26]盛昭瀚,马军海.非线性动力系统分析引论[M].北京:科学出版社,2002:19-33.
[27]王兴元. 复杂非线性系统中的混沌[M]. 北京:电子工业出版社,2003:26-37.
[28]SAKAGUCHI H, TOMITA K. Bifurcation of the couple logistic map[J]. Progress of Theoretical Physics, 1987,78(2):305-309.
[29]赵德平,彭鹏.广义Mandelbrot集和Logistic映射在图像压缩中的应用[J]. 沈阳建筑大学学报:自然科学版, 2008,24(4):694-698.

Medical image registration algorithm based on Powell algorithm and improved genetic algorithm

基于Powell算法与改进遗传算法的医学图像配准方法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

[1]	YANG Hualong, WANG Meiyu, XIN Yuchen. Inventory routing optimization model with heterogeneous vehicles based on horizontal collaboration strategy [J]. Journal of Computer Applications, 2021, 41(10): 3040-3048.
[2]	WANG Yonghang, ZHANG Tianyu, ZHENG Hongxing. Multi-stage rescheduling method of liner considering severe weather [J]. Journal of Computer Applications, 2021, 41(1): 286-294.
[3]	CHENG Yusheng, SONG Fan, WANG Yibin, QIAN Kun. Multi-label feature selection algorithm based on conditional mutual information of expert feature [J]. Journal of Computer Applications, 2020, 40(2): 503-509.
[4]	YONG Juya, ZHOU Zhongmei. Multi-level feature selection algorithm based on mutual information [J]. Journal of Computer Applications, 2020, 40(12): 3478-3484.
[5]	WANG Yu, XU Jianmin. Hot new word discovery applied for detection of network hot news [J]. Journal of Computer Applications, 2020, 40(12): 3513-3519.
[6]	WANG Jianhua, PAN Yujie, SUN Rui. Flexible job-shop green scheduling algorithm considering machine tool depreciation [J]. Journal of Computer Applications, 2020, 40(1): 43-49.
[7]	QIAO Jun, MENG Xuelei, WANG Dongxian, TANG Lin. High-speed train connection optimization for large passenger transport hub based on transfer orientation [J]. Journal of Computer Applications, 2019, 39(9): 2757-2764.
[8]	WEI Jiawang, WANG Xiao, YUAN Yubo. Adaptive window regression method for face feature point positioning [J]. Journal of Computer Applications, 2019, 39(5): 1459-1465.
[9]	MAO Yingchi, CAO Hai, PING Ping, LI Xiaofang. Feature selection based on maximum conditional and joint mutual information [J]. Journal of Computer Applications, 2019, 39(3): 734-741.
[10]	HU Jian, SU Yongdong, HUANG Wenzai, XIAO Peng, LIU Yuting, YANG Benfu. Intrusion detection method based on ensemble transfer learning via weighted mutual information [J]. Journal of Computer Applications, 2019, 39(11): 3310-3315.
[11]	XU Hongfeng, SUN Zhenqiang. Fast feature selection method based on mutual information in multi-label learning [J]. Journal of Computer Applications, 2019, 39(10): 2815-2821.
[12]	LIANG Zhigang, GU Junhua. Medical image registration by integrating modified brain storm optimization algorithm and Powell algorithm [J]. Journal of Computer Applications, 2018, 38(9): 2683-2688.
[13]	TANG Xiaochuan, QIU Xiwei, LUO Liang. Interaction based algorithm for feature selection in text categorization [J]. Journal of Computer Applications, 2018, 38(7): 1857-1861.
[14]	YU Dekuang, YANG Yi. Mutual information maximum value filter criteria combined with particle swarm optimization algorithm in feature gene selection for tumor classification [J]. Journal of Computer Applications, 2018, 38(2): 421-426.
[15]	WANG Zelin, HAO Shuixia. Extracting kernel basis using differential evolution algorithm for packet matching [J]. Journal of Computer Applications, 2017, 37(3): 777-781.