Literature DB >> 26087480

Automatic Fascicle Length Estimation on Muscle Ultrasound Images With an Orientation-Sensitive Segmentation.

Guang-Quan Zhou, Yong-Ping Zheng.   

Abstract

GOAL: The fascicle length obtained by ultrasound imaging is one of the crucial muscle architecture parameters for understanding the contraction mechanics and pathological conditions of muscles. However, the lack of a reliable automatic measurement method restricts the application of the fascicle length for the analysis of the muscle function, as frame-by-frame manual measurement is time-consuming. In this study, we propose an automatic measurement method to preclude the influence of nonfascicle components on the estimation of the fascicle length by using motion estimation of fascicle structures.
METHODS: The method starts with image segmentation using the cohesiveness of fascicle orientation as a feature, obtaining the fascicle change by tracking manually marked points on the fascicular path with the Lucas-Kanade optical flow algorithm applied on the segmented image.
RESULTS: The performance of this method was evaluated on ultrasound images of the gastrocnemius obtained from seven healthy subjects (34.4 ± 5.0 years). Waveform similarity between the manual and dynamic measurements was assessed by calculating the overall similarity with the coefficient of multiple correlations (CMC). In vivo experiments demonstrated that fascicle tracking with the orientation-sensitive segmentation (CMC = 0.97 ± 0.01) was more consistent with the manual measurements than existing automatic methods (CMC = 0.87 ± 0.10).
CONCLUSION: Our method was robust to the interference of nonfascicle components, resulting in a more reliable measurement of the fascicle length. SIGNIFICANCE: The proposed method may facilitate further research and applications related to real-time architectural change of muscles.

Entities:  

Mesh:

Year:  2015        PMID: 26087480     DOI: 10.1109/TBME.2015.2445345

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  5 in total

1.  Lower Limb Motion Estimation Using Ultrasound Imaging: A Framework for Assistive Device Control.

Authors:  Mohammad Hassan Jahanandish; Nicholas P Fey; Kenneth Hoyt
Journal:  IEEE J Biomed Health Inform       Date:  2019-01-09       Impact factor: 5.772

Review 2.  Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey.

Authors:  Qinghua Huang; Fan Zhang; Xuelong Li
Journal:  Biomed Res Int       Date:  2018-03-04       Impact factor: 3.411

3.  Compressed Sensing Image Reconstruction of Ultrasound Image for Treatment of Early Traumatic Myositis Ossificans of Elbow Joint by Electroacupuncture.

Authors:  Yi Zhu; Mengyuan Sheng; Yuanming Ouyang; Lichang Zhong; Kun Liu; Tan Ge; Yaochi Wu
Journal:  J Healthc Eng       Date:  2021-12-07       Impact factor: 2.682

4.  Measurement of Gender Differences of Gastrocnemius Muscle and Tendon Using Sonomyography during Calf Raises: A Pilot Study.

Authors:  Guang-Quan Zhou; Yong-Ping Zheng; Ping Zhou
Journal:  Biomed Res Int       Date:  2017-12-31       Impact factor: 3.411

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

Authors:  Guang-Quan Zhou; Yi Zhang; Ruo-Li Wang; Ping Zhou; Yong-Ping Zheng; Olga Tarassova; Anton Arndt; Qiang Chen
Journal:  Biomed Res Int       Date:  2018-03-19       Impact factor: 3.411

  5 in total

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