Title
Continuous detection of muscle aspect ratio using keypoint tracking in ultrasonography.
Abstract
Muscle aspect ratio of cross-sectional area is one of the most widely used parameters for quantifying muscle function in both diagnosis and rehabilitation assessment. Ultrasound imaging has been frequently used to noninvasively study the characteristics of human muscles as a reliable method. However, the aspect ratio measurement is traditionally conducted by the manual digitization of reference points; thus, it is subjective, time-consuming, and prone to errors. In this paper, a novel method is proposed to continuously detect the muscle aspect ratio. Two keypoint pairs are manually digitized on the lateral and longitudinal borders at the first frame, and automatically tracked by an optical flow technique at the subsequent frames. The muscle aspect ratio is thereby obtained based on the estimated muscle width and thickness. Six ultrasound sequences from different subjects are used to evaluate this method, and the overall coefficient of multiple correlation of the results between manual and proposed methods is 0.97 ± 0.02. The linear regression shows that a good linear correlation between the results of the two methods is obtained (R(2) = 0.974), with difference -0.01 ± 0.16. The method proposed here provides an accurate, high repeatable, and efficient approach for estimating muscle aspect ratio during human motion, thus justifying its application in biological sciences.
Year
DOI
Venue
2013
10.1109/TBME.2013.2256786
IEEE Trans. Biomed. Engineering
Keywords
Field
DocType
ULTRASOUND IMAGES,FASCICLE LENGTH,ARCHITECTURE,THICKNESS,SONOMYOGRAPHY,CONTRACTION,ORIENTATION,RESPONSES,RAMP
Computer vision,Muscle contraction,Ultrasonography,Multiple correlation,Computer science,Regression analysis,Human motion,Artificial intelligence,Optical flow,Linear regression,Ultrasound
Journal
Volume
Issue
ISSN
60
8
1558-2531
Citations 
PageRank 
References 
1
0.44
6
Authors
7
Name
Order
Citations
PageRank
Qiaoliang Li119120.41
Hui-sheng Zhang215924.84
Suwen Qi3112.40
Mingbo Qiu410.44
Xin Chen5109.01
Si-Ping Chen626537.25
Tianfu Wang738255.46