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Automatic Myotendinous Junction Tracking in Ultrasound Images with Phase-Based Segmentation

Displacement of the myotendinous junction (MTJ) obtained by ultrasound imaging is crucial to quantify the interactive length changes of muscles and tendons for understanding the mechanics and pathological conditions of the muscle-tendon unit during motion. However, the lack of a reliable automatic m...

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Autores principales: Zhou, Guang-Quan, Zhang, Yi, Wang, Ruo-Li, Zhou, Ping, Zheng, Yong-Ping, Tarassova, Olga, Arndt, Anton, Chen, Qiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5884232/
https://www.ncbi.nlm.nih.gov/pubmed/29750152
http://dx.doi.org/10.1155/2018/3697835
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author Zhou, Guang-Quan
Zhang, Yi
Wang, Ruo-Li
Zhou, Ping
Zheng, Yong-Ping
Tarassova, Olga
Arndt, Anton
Chen, Qiang
author_facet Zhou, Guang-Quan
Zhang, Yi
Wang, Ruo-Li
Zhou, Ping
Zheng, Yong-Ping
Tarassova, Olga
Arndt, Anton
Chen, Qiang
author_sort Zhou, Guang-Quan
collection PubMed
description Displacement of the myotendinous junction (MTJ) obtained by ultrasound imaging is crucial to quantify the interactive length changes of muscles and tendons for understanding the mechanics and pathological conditions of the muscle-tendon unit during motion. However, the lack of a reliable automatic measurement method restricts its application in human motion analysis. This paper presents an automated measurement of MTJ displacement using prior knowledge on tendinous tissues and MTJ, precluding the influence of nontendinous components on the estimation of MTJ displacement. It is based on the perception of tendinous features from musculoskeletal ultrasound images using Radon transform and thresholding methods, with information about the symmetric measures obtained from phase congruency. The displacement of MTJ is achieved by tracking manually marked points on tendinous tissues with the Lucas-Kanade optical flow algorithm applied over the segmented MTJ region. The performance of this method was evaluated on ultrasound images of the gastrocnemius obtained from 10 healthy subjects (26.0 ± 2.9 years of age). Waveform similarity between the manual and automatic measurements was assessed by calculating the overall similarity with the coefficient of multiple correlation (CMC). In vivo experiments demonstrated that MTJ tracking with the proposed method (CMC = 0.97 ± 0.02) was more consistent with the manual measurements than existing optical flow tracking methods (CMC = 0.79 ± 0.11). This study demonstrated that the proposed method was robust to the interference of nontendinous components, resulting in a more reliable measurement of MTJ displacement, which may facilitate further research and applications related to the architectural change of muscles and tendons.
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spelling pubmed-58842322018-05-10 Automatic Myotendinous Junction Tracking in Ultrasound Images with Phase-Based Segmentation Zhou, Guang-Quan Zhang, Yi Wang, Ruo-Li Zhou, Ping Zheng, Yong-Ping Tarassova, Olga Arndt, Anton Chen, Qiang Biomed Res Int Research Article Displacement of the myotendinous junction (MTJ) obtained by ultrasound imaging is crucial to quantify the interactive length changes of muscles and tendons for understanding the mechanics and pathological conditions of the muscle-tendon unit during motion. However, the lack of a reliable automatic measurement method restricts its application in human motion analysis. This paper presents an automated measurement of MTJ displacement using prior knowledge on tendinous tissues and MTJ, precluding the influence of nontendinous components on the estimation of MTJ displacement. It is based on the perception of tendinous features from musculoskeletal ultrasound images using Radon transform and thresholding methods, with information about the symmetric measures obtained from phase congruency. The displacement of MTJ is achieved by tracking manually marked points on tendinous tissues with the Lucas-Kanade optical flow algorithm applied over the segmented MTJ region. The performance of this method was evaluated on ultrasound images of the gastrocnemius obtained from 10 healthy subjects (26.0 ± 2.9 years of age). Waveform similarity between the manual and automatic measurements was assessed by calculating the overall similarity with the coefficient of multiple correlation (CMC). In vivo experiments demonstrated that MTJ tracking with the proposed method (CMC = 0.97 ± 0.02) was more consistent with the manual measurements than existing optical flow tracking methods (CMC = 0.79 ± 0.11). This study demonstrated that the proposed method was robust to the interference of nontendinous components, resulting in a more reliable measurement of MTJ displacement, which may facilitate further research and applications related to the architectural change of muscles and tendons. Hindawi 2018-03-19 /pmc/articles/PMC5884232/ /pubmed/29750152 http://dx.doi.org/10.1155/2018/3697835 Text en Copyright © 2018 Guang-Quan Zhou et al. https://creativecommons.org/licenses/by/4.0/ 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
Zhou, Guang-Quan
Zhang, Yi
Wang, Ruo-Li
Zhou, Ping
Zheng, Yong-Ping
Tarassova, Olga
Arndt, Anton
Chen, Qiang
Automatic Myotendinous Junction Tracking in Ultrasound Images with Phase-Based Segmentation
title Automatic Myotendinous Junction Tracking in Ultrasound Images with Phase-Based Segmentation
title_full Automatic Myotendinous Junction Tracking in Ultrasound Images with Phase-Based Segmentation
title_fullStr Automatic Myotendinous Junction Tracking in Ultrasound Images with Phase-Based Segmentation
title_full_unstemmed Automatic Myotendinous Junction Tracking in Ultrasound Images with Phase-Based Segmentation
title_short Automatic Myotendinous Junction Tracking in Ultrasound Images with Phase-Based Segmentation
title_sort automatic myotendinous junction tracking in ultrasound images with phase-based segmentation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5884232/
https://www.ncbi.nlm.nih.gov/pubmed/29750152
http://dx.doi.org/10.1155/2018/3697835
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