Compounding local invariant features and global deformable geometry for medical image registration.

Using deformable models to register medical images can result in problems of initialization of deformable models and robustness and accuracy of matching of inter-subject anatomical variability. To tackle these problems, a novel model is proposed in this paper by compounding local invariant features...

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Autores principales:	Jianhua Zhang, Lei Chen, Xiaoyan Wang, Zhongzhao Teng, Adam J Brown, Jonathan H Gillard, Qiu Guan, Shengyong Chen
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2ccb07cff0344b21a29d7e19165b0ce3
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id	oai:doaj.org-article:2ccb07cff0344b21a29d7e19165b0ce3
record_format	dspace
spelling	oai:doaj.org-article:2ccb07cff0344b21a29d7e19165b0ce32021-11-25T06:02:51ZCompounding local invariant features and global deformable geometry for medical image registration.1932-620310.1371/journal.pone.0105815https://doaj.org/article/2ccb07cff0344b21a29d7e19165b0ce32014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/25165985/?tool=EBIhttps://doaj.org/toc/1932-6203Using deformable models to register medical images can result in problems of initialization of deformable models and robustness and accuracy of matching of inter-subject anatomical variability. To tackle these problems, a novel model is proposed in this paper by compounding local invariant features and global deformable geometry. This model has four steps. First, a set of highly-repeatable and highly-robust local invariant features, called Key Features Model (KFM), are extracted by an effective matching strategy. Second, local features can be matched more accurately through the KFM for the purpose of initializing a global deformable model. Third, the positional relationship between the KFM and the global deformable model can be used to precisely pinpoint all landmarks after initialization. And fourth, the final pose of the global deformable model is determined by an iterative process with a lower time cost. Through the practical experiments, the paper finds three important conclusions. First, it proves that the KFM can detect the matching feature points well. Second, the precision of landmark locations adjusted by the modeled relationship between KFM and global deformable model is greatly improved. Third, regarding the fitting accuracy and efficiency, by observation from the practical experiments, it is found that the proposed method can improve 6~8% of the fitting accuracy and reduce around 50% of the computational time compared with state-of-the-art methods.Jianhua ZhangLei ChenXiaoyan WangZhongzhao TengAdam J BrownJonathan H GillardQiu GuanShengyong ChenPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 8, p e105815 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jianhua Zhang Lei Chen Xiaoyan Wang Zhongzhao Teng Adam J Brown Jonathan H Gillard Qiu Guan Shengyong Chen Compounding local invariant features and global deformable geometry for medical image registration.
description	Using deformable models to register medical images can result in problems of initialization of deformable models and robustness and accuracy of matching of inter-subject anatomical variability. To tackle these problems, a novel model is proposed in this paper by compounding local invariant features and global deformable geometry. This model has four steps. First, a set of highly-repeatable and highly-robust local invariant features, called Key Features Model (KFM), are extracted by an effective matching strategy. Second, local features can be matched more accurately through the KFM for the purpose of initializing a global deformable model. Third, the positional relationship between the KFM and the global deformable model can be used to precisely pinpoint all landmarks after initialization. And fourth, the final pose of the global deformable model is determined by an iterative process with a lower time cost. Through the practical experiments, the paper finds three important conclusions. First, it proves that the KFM can detect the matching feature points well. Second, the precision of landmark locations adjusted by the modeled relationship between KFM and global deformable model is greatly improved. Third, regarding the fitting accuracy and efficiency, by observation from the practical experiments, it is found that the proposed method can improve 6~8% of the fitting accuracy and reduce around 50% of the computational time compared with state-of-the-art methods.
format	article
author	Jianhua Zhang Lei Chen Xiaoyan Wang Zhongzhao Teng Adam J Brown Jonathan H Gillard Qiu Guan Shengyong Chen
author_facet	Jianhua Zhang Lei Chen Xiaoyan Wang Zhongzhao Teng Adam J Brown Jonathan H Gillard Qiu Guan Shengyong Chen
author_sort	Jianhua Zhang
title	Compounding local invariant features and global deformable geometry for medical image registration.
title_short	Compounding local invariant features and global deformable geometry for medical image registration.
title_full	Compounding local invariant features and global deformable geometry for medical image registration.
title_fullStr	Compounding local invariant features and global deformable geometry for medical image registration.
title_full_unstemmed	Compounding local invariant features and global deformable geometry for medical image registration.
title_sort	compounding local invariant features and global deformable geometry for medical image registration.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/2ccb07cff0344b21a29d7e19165b0ce3
work_keys_str_mv	AT jianhuazhang compoundinglocalinvariantfeaturesandglobaldeformablegeometryformedicalimageregistration AT leichen compoundinglocalinvariantfeaturesandglobaldeformablegeometryformedicalimageregistration AT xiaoyanwang compoundinglocalinvariantfeaturesandglobaldeformablegeometryformedicalimageregistration AT zhongzhaoteng compoundinglocalinvariantfeaturesandglobaldeformablegeometryformedicalimageregistration AT adamjbrown compoundinglocalinvariantfeaturesandglobaldeformablegeometryformedicalimageregistration AT jonathanhgillard compoundinglocalinvariantfeaturesandglobaldeformablegeometryformedicalimageregistration AT qiuguan compoundinglocalinvariantfeaturesandglobaldeformablegeometryformedicalimageregistration AT shengyongchen compoundinglocalinvariantfeaturesandglobaldeformablegeometryformedicalimageregistration
_version_	1718414213645336576

Compounding local invariant features and global deformable geometry for medical image registration.

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