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...
| Publicado en: | BioMed Research International pp. 1 - 16 |
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| Autores principales: | , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
10/30/2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=153339499&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153339499 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 10/30/2021 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 153339499 153339499 153339499 10.1155/2021/6654247 153339499 ppf: 1 ppct: 15 formats: fmt: @attributes: type: P tig: atl: Modeling Respiratory Signals by Deformable Image Registration on 4DCT Lung Images. aug: au: Bao, Pham The Trang, Hoang Thi Kieu Tuan, Tran Anh Thanh, Tran Thien Hai, Vo Hong affil: Computer Science Department, Information Science Faculty, Sai Gon University, Ho Chi Minh City, Vietnam sug: subj: Lung Neoplasms Tomography, X-Ray Computed Imaging, Three-Dimensional ab: 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. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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