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 Chao1*, LI Guangyao1, TAN Yunlan2,1, XU Xianglong1   

  1. 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



  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:


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: