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Robust Adaptive Principal Component Analysis Based on Intergraph Matrix for Medical Image Registration

This paper proposes a novel robust adaptive principal component analysis (RAPCA) method based on intergraph matrix for image registration in order to improve robustness and real-time performance. The contributions can be divided into three parts. Firstly, a novel RAPCA method is developed to capture...

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Detalles Bibliográficos
Autores principales: Leng, Chengcai, Xiao, Jinjun, Li, Min, Zhang, Haipeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4417574/
https://www.ncbi.nlm.nih.gov/pubmed/25960739
http://dx.doi.org/10.1155/2015/829528
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author Leng, Chengcai
Xiao, Jinjun
Li, Min
Zhang, Haipeng
author_facet Leng, Chengcai
Xiao, Jinjun
Li, Min
Zhang, Haipeng
author_sort Leng, Chengcai
collection PubMed
description This paper proposes a novel robust adaptive principal component analysis (RAPCA) method based on intergraph matrix for image registration in order to improve robustness and real-time performance. The contributions can be divided into three parts. Firstly, a novel RAPCA method is developed to capture the common structure patterns based on intergraph matrix of the objects. Secondly, the robust similarity measure is proposed based on adaptive principal component. Finally, the robust registration algorithm is derived based on the RAPCA. The experimental results show that the proposed method is very effective in capturing the common structure patterns for image registration on real-world images.
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spelling pubmed-44175742015-05-10 Robust Adaptive Principal Component Analysis Based on Intergraph Matrix for Medical Image Registration Leng, Chengcai Xiao, Jinjun Li, Min Zhang, Haipeng Comput Intell Neurosci Research Article This paper proposes a novel robust adaptive principal component analysis (RAPCA) method based on intergraph matrix for image registration in order to improve robustness and real-time performance. The contributions can be divided into three parts. Firstly, a novel RAPCA method is developed to capture the common structure patterns based on intergraph matrix of the objects. Secondly, the robust similarity measure is proposed based on adaptive principal component. Finally, the robust registration algorithm is derived based on the RAPCA. The experimental results show that the proposed method is very effective in capturing the common structure patterns for image registration on real-world images. Hindawi Publishing Corporation 2015 2015-04-19 /pmc/articles/PMC4417574/ /pubmed/25960739 http://dx.doi.org/10.1155/2015/829528 Text en Copyright © 2015 Chengcai Leng 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
Leng, Chengcai
Xiao, Jinjun
Li, Min
Zhang, Haipeng
Robust Adaptive Principal Component Analysis Based on Intergraph Matrix for Medical Image Registration
title Robust Adaptive Principal Component Analysis Based on Intergraph Matrix for Medical Image Registration
title_full Robust Adaptive Principal Component Analysis Based on Intergraph Matrix for Medical Image Registration
title_fullStr Robust Adaptive Principal Component Analysis Based on Intergraph Matrix for Medical Image Registration
title_full_unstemmed Robust Adaptive Principal Component Analysis Based on Intergraph Matrix for Medical Image Registration
title_short Robust Adaptive Principal Component Analysis Based on Intergraph Matrix for Medical Image Registration
title_sort robust adaptive principal component analysis based on intergraph matrix for medical image registration
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4417574/
https://www.ncbi.nlm.nih.gov/pubmed/25960739
http://dx.doi.org/10.1155/2015/829528
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AT xiaojinjun robustadaptiveprincipalcomponentanalysisbasedonintergraphmatrixformedicalimageregistration
AT limin robustadaptiveprincipalcomponentanalysisbasedonintergraphmatrixformedicalimageregistration
AT zhanghaipeng robustadaptiveprincipalcomponentanalysisbasedonintergraphmatrixformedicalimageregistration