Volume 7, Issue 4
Performance Comparison of Optimization Methods for Medical Image Registration

Meisen Pan, Jianjun Jiang, Fen Zhang & Xia Fang

Journal of Fiber Bioengineering & Informatics, 7 (2014), pp. 507-516.

Published online: 2014-07

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  • Abstract

In the process of medical image registration, the registration function (also so-called similarity metric) was taken as the objective function, and the multi-parameter optimization method as the tool for obtaining the optimal transformation parameters. In this paper, by the use of the mutual information as the registration function, the Powell method and the genetic algorithm were exerted to explore the optimal transformation parameters respectively, and their optimizing performances were evaluated and compared. The experimental results reveal that the Powell method can cater to both the mono- and multi-modality medical image registrations. Unfortunately, however, the genetic algorithm is not adopted for the medical image registration regardless of the registration accuracy or the running time and needs to be significantly improved.

  • Keywords

Medical Image Registration Mutual Information Optimization Method The Powell Method

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COPYRIGHT: © Global Science Press

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@Article{JFBI-7-507, author = {}, title = {Performance Comparison of Optimization Methods for Medical Image Registration}, journal = {Journal of Fiber Bioengineering and Informatics}, year = {2014}, volume = {7}, number = {4}, pages = {507--516}, abstract = {In the process of medical image registration, the registration function (also so-called similarity metric) was taken as the objective function, and the multi-parameter optimization method as the tool for obtaining the optimal transformation parameters. In this paper, by the use of the mutual information as the registration function, the Powell method and the genetic algorithm were exerted to explore the optimal transformation parameters respectively, and their optimizing performances were evaluated and compared. The experimental results reveal that the Powell method can cater to both the mono- and multi-modality medical image registrations. Unfortunately, however, the genetic algorithm is not adopted for the medical image registration regardless of the registration accuracy or the running time and needs to be significantly improved.}, issn = {2617-8699}, doi = {https://doi.org/10.3993/jfbi12201404}, url = {http://global-sci.org/intro/article_detail/jfbi/4805.html} }
TY - JOUR T1 - Performance Comparison of Optimization Methods for Medical Image Registration JO - Journal of Fiber Bioengineering and Informatics VL - 4 SP - 507 EP - 516 PY - 2014 DA - 2014/07 SN - 7 DO - http://doi.org/10.3993/jfbi12201404 UR - https://global-sci.org/intro/article_detail/jfbi/4805.html KW - Medical Image Registration KW - Mutual Information KW - Optimization Method KW - The Powell Method AB - In the process of medical image registration, the registration function (also so-called similarity metric) was taken as the objective function, and the multi-parameter optimization method as the tool for obtaining the optimal transformation parameters. In this paper, by the use of the mutual information as the registration function, the Powell method and the genetic algorithm were exerted to explore the optimal transformation parameters respectively, and their optimizing performances were evaluated and compared. The experimental results reveal that the Powell method can cater to both the mono- and multi-modality medical image registrations. Unfortunately, however, the genetic algorithm is not adopted for the medical image registration regardless of the registration accuracy or the running time and needs to be significantly improved.
Meisen Pan, Jianjun Jiang, Fen Zhang & Xia Fang. (2019). Performance Comparison of Optimization Methods for Medical Image Registration. Journal of Fiber Bioengineering and Informatics. 7 (4). 507-516. doi:10.3993/jfbi12201404
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