Modeling Respiratory Signals by Deformable Image Registration on 4DCT Lung Images

The lung organ of human anatomy captured by a medical device reveals inhalation and exhalation information for treatment and monitoring. Given a large number of slices covering an area of the lung, we have a set of three-dimensional lung data. And then, by combining additionally with breath-hold mea...

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Autores principales:	Pham The Bao, Hoang Thi Kieu Trang, Tran Anh Tuan, Tran Thien Thanh, Vo Hong Hai
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Medicine R
Acceso en línea:	https://doaj.org/article/4a691a5a5bca4671b9a8313578316ca1
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id	oai:doaj.org-article:4a691a5a5bca4671b9a8313578316ca1
record_format	dspace
spelling	oai:doaj.org-article:4a691a5a5bca4671b9a8313578316ca12021-11-08T02:35:59ZModeling Respiratory Signals by Deformable Image Registration on 4DCT Lung Images2314-614110.1155/2021/6654247https://doaj.org/article/4a691a5a5bca4671b9a8313578316ca12021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/6654247https://doaj.org/toc/2314-6141The lung organ of human anatomy captured by a medical device reveals inhalation and exhalation information for treatment and monitoring. Given a large number of slices covering an area of the lung, we have a set of three-dimensional lung data. And then, by combining additionally with breath-hold measurements, we have a dataset of multigroup CT images (called 4DCT image set) that could show the lung motion and deformation over time. Up to now, it has still been a challenging problem to model a respiratory signal representing patients’ breathing motion as well as simulating inhalation and exhalation process from 4DCT lung images because of its complexity. In this paper, we propose a promising hybrid approach incorporating the local binary pattern (LBP) histogram with entropy comparison to register the lung images. The segmentation process of the left and right lung is completely overcome by the minimum variance quantization and within class variance techniques which help the registration stage. The experiments are conducted on the 4DCT deformable image registration (DIR) public database giving us the overall evaluation on each stage: segmentation, registration, and modeling, to validate the effectiveness of the approach.Pham The BaoHoang Thi Kieu TrangTran Anh TuanTran Thien ThanhVo Hong HaiHindawi LimitedarticleMedicineRENBioMed Research International, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R
spellingShingle	Medicine R Pham The Bao Hoang Thi Kieu Trang Tran Anh Tuan Tran Thien Thanh Vo Hong Hai Modeling Respiratory Signals by Deformable Image Registration on 4DCT Lung Images
description	The lung organ of human anatomy captured by a medical device reveals inhalation and exhalation information for treatment and monitoring. Given a large number of slices covering an area of the lung, we have a set of three-dimensional lung data. And then, by combining additionally with breath-hold measurements, we have a dataset of multigroup CT images (called 4DCT image set) that could show the lung motion and deformation over time. Up to now, it has still been a challenging problem to model a respiratory signal representing patients’ breathing motion as well as simulating inhalation and exhalation process from 4DCT lung images because of its complexity. In this paper, we propose a promising hybrid approach incorporating the local binary pattern (LBP) histogram with entropy comparison to register the lung images. The segmentation process of the left and right lung is completely overcome by the minimum variance quantization and within class variance techniques which help the registration stage. The experiments are conducted on the 4DCT deformable image registration (DIR) public database giving us the overall evaluation on each stage: segmentation, registration, and modeling, to validate the effectiveness of the approach.
format	article
author	Pham The Bao Hoang Thi Kieu Trang Tran Anh Tuan Tran Thien Thanh Vo Hong Hai
author_facet	Pham The Bao Hoang Thi Kieu Trang Tran Anh Tuan Tran Thien Thanh Vo Hong Hai
author_sort	Pham The Bao
title	Modeling Respiratory Signals by Deformable Image Registration on 4DCT Lung Images
title_short	Modeling Respiratory Signals by Deformable Image Registration on 4DCT Lung Images
title_full	Modeling Respiratory Signals by Deformable Image Registration on 4DCT Lung Images
title_fullStr	Modeling Respiratory Signals by Deformable Image Registration on 4DCT Lung Images
title_full_unstemmed	Modeling Respiratory Signals by Deformable Image Registration on 4DCT Lung Images
title_sort	modeling respiratory signals by deformable image registration on 4dct lung images
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/4a691a5a5bca4671b9a8313578316ca1
work_keys_str_mv	AT phamthebao modelingrespiratorysignalsbydeformableimageregistrationon4dctlungimages AT hoangthikieutrang modelingrespiratorysignalsbydeformableimageregistrationon4dctlungimages AT trananhtuan modelingrespiratorysignalsbydeformableimageregistrationon4dctlungimages AT tranthienthanh modelingrespiratorysignalsbydeformableimageregistrationon4dctlungimages AT vohonghai modelingrespiratorysignalsbydeformableimageregistrationon4dctlungimages
_version_	1718443179963842560

Modeling Respiratory Signals by Deformable Image Registration on 4DCT Lung Images

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