Performance Comparison of Optimization Methods for Medical Image Registration
DOI:
10.3993/jfbi12201404
Journal of Fiber Bioengineering & Informatics, 7 (2014), pp. 507-516.
Published online: 2014-07
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@Article{JFBI-7-507,
author = {Meisen Pan, Jianjun Jiang, Fen Zhang and Xia Fang},
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
AU - Meisen Pan, Jianjun Jiang, Fen Zhang & Xia Fang
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 and Xia Fang. (2014). 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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