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Hybrid Particle Swarm Optimization and Its Application to Multimodal 3D Medical Image Registration
In the area of medical image analysis, 3D multimodality image registration is an important issue. In the processing of registration, an optimization approach has been applied to estimate the transformation of the reference image and target image. Some local optimization techniques are frequently use...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3432335/ https://www.ncbi.nlm.nih.gov/pubmed/22997508 http://dx.doi.org/10.1155/2012/561406 |
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author | Lin, Chen-Lun Mimori, Aya Chen, Yen-Wei |
author_facet | Lin, Chen-Lun Mimori, Aya Chen, Yen-Wei |
author_sort | Lin, Chen-Lun |
collection | PubMed |
description | In the area of medical image analysis, 3D multimodality image registration is an important issue. In the processing of registration, an optimization approach has been applied to estimate the transformation of the reference image and target image. Some local optimization techniques are frequently used, such as the gradient descent method. However, these methods need a good initial value in order to avoid the local resolution. In this paper, we present a new improved global optimization approach named hybrid particle swarm optimization (HPSO) for medical image registration, which includes two concepts of genetic algorithms—subpopulation and crossover. |
format | Online Article Text |
id | pubmed-3432335 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-34323352012-09-20 Hybrid Particle Swarm Optimization and Its Application to Multimodal 3D Medical Image Registration Lin, Chen-Lun Mimori, Aya Chen, Yen-Wei Comput Intell Neurosci Research Article In the area of medical image analysis, 3D multimodality image registration is an important issue. In the processing of registration, an optimization approach has been applied to estimate the transformation of the reference image and target image. Some local optimization techniques are frequently used, such as the gradient descent method. However, these methods need a good initial value in order to avoid the local resolution. In this paper, we present a new improved global optimization approach named hybrid particle swarm optimization (HPSO) for medical image registration, which includes two concepts of genetic algorithms—subpopulation and crossover. Hindawi Publishing Corporation 2012 2012-08-22 /pmc/articles/PMC3432335/ /pubmed/22997508 http://dx.doi.org/10.1155/2012/561406 Text en Copyright © 2012 Chen-Lun Lin et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Lin, Chen-Lun Mimori, Aya Chen, Yen-Wei Hybrid Particle Swarm Optimization and Its Application to Multimodal 3D Medical Image Registration |
title | Hybrid Particle Swarm Optimization and Its Application to Multimodal 3D Medical Image Registration |
title_full | Hybrid Particle Swarm Optimization and Its Application to Multimodal 3D Medical Image Registration |
title_fullStr | Hybrid Particle Swarm Optimization and Its Application to Multimodal 3D Medical Image Registration |
title_full_unstemmed | Hybrid Particle Swarm Optimization and Its Application to Multimodal 3D Medical Image Registration |
title_short | Hybrid Particle Swarm Optimization and Its Application to Multimodal 3D Medical Image Registration |
title_sort | hybrid particle swarm optimization and its application to multimodal 3d medical image registration |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3432335/ https://www.ncbi.nlm.nih.gov/pubmed/22997508 http://dx.doi.org/10.1155/2012/561406 |
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