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

Autor: Yung-Nien Sun, Jian-Han Hsu, Li-Chieh Kuo, Bo-I Chuang, I-Ming Jou, Fong-Chin Su
Rok vydání: 2016
Předmět:
Engineering
lcsh:Medical technology
Matching (graph theory)
Movement
0206 medical engineering
Biomedical Engineering
Optical flow
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
02 engineering and technology
Optic Flow
Tracking (particle physics)
Sensitivity and Specificity
Displacement (vector)
030218 nuclear medicine & medical imaging
Pattern Recognition
Automated
Biomaterials
Machine Learning
Tendons
03 medical and health sciences
0302 clinical medicine
Match moving
Image Interpretation
Computer-Assisted
Ultrasound
Cadaver
Humans
Radiology
Nuclear Medicine and imaging
Computer vision
Block (data storage)
Ultrasonography
Radiological and Ultrasound Technology
business.industry
Research
Frame (networking)
Reproducibility of Results
Block matching
Speckle noise
Tendon tracking
General Medicine
020601 biomedical engineering
lcsh:R855-855.5
Subtraction Technique
Artificial intelligence
business
Algorithms
Zdroj: BioMedical Engineering
BioMedical Engineering OnLine, Vol 16, Iss 1, Pp 1-19 (2017)
ISSN: 1475-925X
Popis: 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 0.05 mm. 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. Electronic supplementary material The online version of this article (doi:10.1186/s12938-017-0335-x) contains supplementary material, which is available to authorized users.
Databáze: OpenAIRE
Externí odkaz: