Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Lin, Chen-Lun, Mimori, Aya, Chen, Yen-Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2012
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
_version_ 1782242192756047872
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
work_keys_str_mv AT linchenlun hybridparticleswarmoptimizationanditsapplicationtomultimodal3dmedicalimageregistration
AT mimoriaya hybridparticleswarmoptimizationanditsapplicationtomultimodal3dmedicalimageregistration
AT chenyenwei hybridparticleswarmoptimizationanditsapplicationtomultimodal3dmedicalimageregistration