Tendon-motion tracking in an ultrasound image sequence using optical-flow-based block matching

Bo I. Chuang, Jian Han Hsu, Li Chieh Kuo, I. Ming Jou, Fong Chin Su, Yung Nien Sun

研究成果: Article

2 引文 (Scopus)

摘要

Background: Tendon motion, which is commonly observed using ultrasound imaging, is one of the most important features used in tendinopathy diagnosis. However, speckle noise and out-of-plane issues make the tracking process difficult. Manual tracking is usually time consuming and often yields inconsistent results between users. Methods: To automatically track tendon motion in ultrasound images, we developed a new method that combines the advantages of optical flow and multi-kernel block matching. For every pair of adjacent image frames, the optical flow is computed and used to estimate the accumulated displacement. The proposed method selects the frame interval adaptively based on this displacement. Multi-kernel block matching is then computed on the two selected frames, and, to reduce tracking errors, the detailed displacements of the frames in between are interpolated based on the optical flow results. Results: In the experiments, cadaver data were used to evaluate the tracking results. The mean absolute error was less than 0.05mm. The proposed method also tracked the motion of tendons in vivo, which provides useful information for clinical diagnosis. Conclusion: The proposed method provides a new index for adaptively determining the frame interval. Compared with other methods, the proposed method yields tracking results that are significantly more accurate.

原文English
文章編號47
期刊Biomedical engineering online
16
發行號1
DOIs
出版狀態Published - 2017 四月 20

指紋

Optical flows
Tendons
Ultrasonics
Speckle
Imaging techniques
Tendinopathy
Cadaver
Experiments
Ultrasonography

All Science Journal Classification (ASJC) codes

  • Radiological and Ultrasound Technology
  • Biomaterials
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

引用此文

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abstract = "Background: Tendon motion, which is commonly observed using ultrasound imaging, is one of the most important features used in tendinopathy diagnosis. However, speckle noise and out-of-plane issues make the tracking process difficult. Manual tracking is usually time consuming and often yields inconsistent results between users. Methods: To automatically track tendon motion in ultrasound images, we developed a new method that combines the advantages of optical flow and multi-kernel block matching. For every pair of adjacent image frames, the optical flow is computed and used to estimate the accumulated displacement. The proposed method selects the frame interval adaptively based on this displacement. Multi-kernel block matching is then computed on the two selected frames, and, to reduce tracking errors, the detailed displacements of the frames in between are interpolated based on the optical flow results. Results: In the experiments, cadaver data were used to evaluate the tracking results. The mean absolute error was less than 0.05mm. The proposed method also tracked the motion of tendons in vivo, which provides useful information for clinical diagnosis. Conclusion: The proposed method provides a new index for adaptively determining the frame interval. Compared with other methods, the proposed method yields tracking results that are significantly more accurate.",
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AU - Hsu, Jian Han

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AU - Jou, I. Ming

AU - Su, Fong Chin

AU - Sun, Yung Nien

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N2 - Background: Tendon motion, which is commonly observed using ultrasound imaging, is one of the most important features used in tendinopathy diagnosis. However, speckle noise and out-of-plane issues make the tracking process difficult. Manual tracking is usually time consuming and often yields inconsistent results between users. Methods: To automatically track tendon motion in ultrasound images, we developed a new method that combines the advantages of optical flow and multi-kernel block matching. For every pair of adjacent image frames, the optical flow is computed and used to estimate the accumulated displacement. The proposed method selects the frame interval adaptively based on this displacement. Multi-kernel block matching is then computed on the two selected frames, and, to reduce tracking errors, the detailed displacements of the frames in between are interpolated based on the optical flow results. Results: In the experiments, cadaver data were used to evaluate the tracking results. The mean absolute error was less than 0.05mm. The proposed method also tracked the motion of tendons in vivo, which provides useful information for clinical diagnosis. Conclusion: The proposed method provides a new index for adaptively determining the frame interval. Compared with other methods, the proposed method yields tracking results that are significantly more accurate.

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