Name
Title | ||
|---|---|---|
Automatic tracking of aponeuroses and estimation of muscle thickness in ultrasonography: a feasibility study. |
Abstract | ||
|---|---|---|
Muscle thickness measurement in ultrasonography was traditionally conducted by a trained operator, and the manual detecting process is time consuming and subjective. In this paper, we proposed an automatic tracking strategy to achieve the continuous and quantitative measurement for gastrocnemius muscle thickness in ultrasound images. The method involved three steps: tracking of seed points, contours extraction of aponeuroses, and muscle thickness estimation. In an ultrasound image sequence, we first selected two seed points in the first frame manually for the superficial and deep aponeuroses, respectively. Seed points in all following frames were then tracked by registering to their respective previous frames. Second, we adopted the local and global intensity fitting model to extract the contours of aponeuroses. At last, the muscle thickness was achieved by calculating the distance between the contours of superficial and deep aponeuroses. The performance of the algorithm was evaluated using 500 frames of ultrasound images. It was demonstrated in the experiments that the proposed methods could be used for objective tracking of aponeuroses and estimation of muscle thickness in musculoskeletal ultrasound images. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/JBHI.2013.2253787 | IEEE J. Biomedical and Health Informatics |
Keywords | Field | DocType |
continuous measurement,ultrasonic imaging,orthopaedics,local intensity fitting model,muscle thickness,automatic tracking,active contour,image segmentation,global intensity fitting model,muscle thickness estimation,biomedical ultrasonics,quantitative measurement,ultrasonography,musculoskeletal ultrasound images,free-form deformation tracking,bone,sonomyography (smg),ultrasound image sequence,contour extraction,image sequences,seed point tracking,gastrocnemius muscle thickness,ultrasound imaging/ultrasonography,aponeurose contours,superficial aponeuroses,image registration,muscle,performance algorithm,deep aponeuroses,automatic tracking strategy,medical image processing,muscle thickness measurement | Gastrocnemius muscle,Active contour model,Computer vision,Ultrasonography,Computer science,Musculoskeletal ultrasound,Image segmentation,Artificial intelligence,Image registration,Ultrasound image,Ultrasound | Journal |
Volume | Issue | ISSN |
17 | 6 | 2168-2208 |
Citations | PageRank | References |
2 | 0.51 | 5 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shan Ling | 1 | 2 | 0.51 |
| Yongjin Zhou | 2 | 17 | 9.89 |
| Ye Chen | 3 | 2 | 0.51 |
| Yu-Qian Zhao | 4 | 92 | 9.98 |
| Lei Wang | 5 | 253 | 28.37 |
| Yong-Ping Zheng | 6 | 119 | 23.74 |