title: Estimation of muscle fiber orientation in ultrasound images using revoting hough transform (RVHT).
abstract: Ultrasound imaging has been used frequently for the study of muscle contraction, including measurements of pennation angles and fascicle orientations. However, these measurements were traditionally conducted by manually drawing lines on the ultrasound images. In this study, we proposed a modified Hough transform (HT), aiming at automatically estimating orientations of straight line-shaped patterns, such as muscle fibers and muscle-bone interface in ultrasound images. The new method first located the global maximum in the HT accumulator matrix, which corresponded to the most dominant collinear feature points globally, using the standard HT; then the pixels close to the detected line were removed from the edge map, the HT accumulator matrix was calculated again, i.e., revoting, and a new line was detected. The iteration was repeated until the predefined termination conditions were satisfied. The performance of the algorithm was tested using computer-generated images with different levels of noises, as well as clinical ultrasound images, and compared with that of the conventional method. It was found that the orientation estimation results obtained by the new algorithm were well correlated (R2 = 0.965), with those obtained using the traditional method, i.e., drawing lines manually and reading the angles with the assistance of software. Further mean-difference plots revealed a difference of 0.18 +/- 2.41 degrees between the two methods at the 95% confidence level. Compared with the traditional method, the new algorithm was more capable of handling with highly noisy data and could avoid the aliasing problem, i.e., reporting multiple lines instead of single expected line. The results of this study suggested that the proposed revoting HT can be potentially used for the reliable and nonsubjective automatic estimation of the orientations of muscle fibers in musculoskeletal ultrasound images.
authors.affiliation: Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong.
authors.forename: Yongjin
authors.initials: Y
authors.lastname: Zhou
authors.forename: Yong-Ping
authors.initials: YP
authors.lastname: Zheng
mesh_terms: D000465:Algorithms; D003196:Computer Graphics; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D018485:Muscle Fibers, Skeletal / Q000000981:diagnostic imaging*; D009949:Orientation; D010363:Pattern Recognition, Automated / Q000379:methods*; D014463:Ultrasonography