An efficient framework for estimation of muscle fiber orientation using ultrasonography.
BACKGROUND
Muscle fiber orientation (MFO) is an important parameter related to musculoskeletal functions. The traditional manual method for MFO estimation in sonograms was labor-intensive. The automatic methods proposed in recent years also involved voting procedures which were computationally expensive.


METHODS
In this paper, we proposed a new framework to efficiently estimate MFO in sonograms. We firstly employed Multi-scale Vessel Enhancement Filtering (MVEF) to enhance fascicles in the sonograms and then the enhanced images were binarized. Finally, line-shaped patterns in the binary map were detected one by one, according to their shape properties. Specifically speaking, for the long-and-thinner regions, the orientation of the targeted muscle fibre was directly computed, without voting procedures, as the orientation of the ellipse that had the same normalized second central moments as the region. For other cases, the Hough voting procedure might be employed for orientation estimation. The performance of the algorithm was evaluated using four various group of sonograms, which are a dataset used in previous reports, 33 sonograms of gastrocnemius from 11 young healthy subjects, one sonogram sequence including 200 frames from a subject and 256 frames from an aged subject with cerebral infarction respectively.


RESULTS
It was demonstrated in the experiments that measurements of the proposed method agreed well with those of the manual method and achieved much more efficiency than the previous Re-voting Hough Transform (RVHT) algorithm.


CONCLUSIONS
Results of the experiments suggested that, without compromising the accuracy, in the proposed framework the previous orientation estimation algorithm was accelerated by reduction of its dependence on voting procedures.
The Shenzhen Key Laboratory for Low-cost Healthcare, Shenzhen, China. yj.zhou@siat.ac.cn.
Shan
S
Ling
Bin
B
Chen
Yongjin
Y
Zhou
Wan-Zhang
WZ
Yang
Yu-Qian
YQ
Zhao
Lei
L
Wang
Yong-Ping
YP
Zheng
D000328:Adult; D000368:Aged; D002544:Cerebral Infarction / Q000000981:diagnostic imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D018485:Muscle Fibers, Skeletal / Q000166:cytology / Q000000981:diagnostic imaging*; D014463:Ultrasonography / Q000379:methods*