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

Autor: Bao PT; Computer Science Department, Information Science Faculty, Sai Gon University, Ho Chi Minh City, Vietnam., Trang HTK; Department of Nuclear Physics, University of Science, Ho Chi Minh City, Vietnam.; Vietnam National University, Ho Chi Minh City, Vietnam., Tuan TA; Vietnam National University, Ho Chi Minh City, Vietnam.; Department of Computer Science, University of Science, Ho Chi Minh City, Vietnam., Thanh TT; Department of Nuclear Physics, University of Science, Ho Chi Minh City, Vietnam.; Vietnam National University, Ho Chi Minh City, Vietnam., Hai VH; Department of Nuclear Physics, University of Science, Ho Chi Minh City, Vietnam.; Vietnam National University, Ho Chi Minh City, Vietnam.
Jazyk: angličtina
Zdroj: BioMed research international [Biomed Res Int] 2021 Oct 30; Vol. 2021, pp. 6654247. Date of Electronic Publication: 2021 Oct 30 (Print Publication: 2021).
DOI: 10.1155/2021/6654247
Abstrakt: 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.
Competing Interests: The authors declare that they have no conflicts of interest.
(Copyright © 2021 Pham The Bao et al.)
Databáze: MEDLINE
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