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.
The flowchart of the proposed strategy for muscle thickness measurement via ultrasonography is shown in Fig. 1. For each frame from the studied ultrasonography sequence, the first step was to acquire two seed points (one on the superficial aponeurosis, and the other on the deep aponeurosis). Then, contours of aponeuroses were extracted and muscle thickness was achieved by calculating the distance between contours of superficial and deep aponeuroses.
Fig. 2. Schematic diagrams of proposed strategy and manual method for mus- cle thickness measurement. (a) Our strategy for muscle thickness measurement. Muscle thickness was estimated by calculating the average distance between contours of superficial and deep aponeuroses. (b) Manual method for muscle thickness measurement. Muscle thickness was approximated as the distance between two points on lower edge of superficial aponeurosis and upper edge of deep aponeurosis, respectively.
The ultrasound probe was fixed by a custom-designed foam container with fixing straps, and a very generous amount of ultrasound gel was applied to secure acoustic coupling between the probe and skin during muscle contractions, as shown in Fig. 3. The probe was adjusted to optimize the contrast of muscle fascicles in ultrasound images. Then, the B-mode ultrasound images were digitized by a video card (NI PCI-1411, National Instruments, Austin, TX, USA) at a rate of 25 frames/s for later analysis.
Fig. 5. Representative set of results of the LGIF model for aponeuroses segmentation. (a) Original image with initial contour. (b) Curve evolution result after 50 iterations. (c) Curve evolution result after 300 iterations. (d) Final contour after evolving stopped.
Previous studies for muscle thickness measurement using either manual or computer-aided methods, estimate muscle thickness at one or several locations, ignoring the fact that muscle thickness changes longitudinally along aponeuroses. It can be seen from Fig. 6 that muscle thickness is not constant along the aponeuroses. Therefore, a new strategy was proposed to estimate muscle thickness for each point along the longitudinal axis of the ultrasound image based on extracting contours of aponeuroses, which would be very useful when interested in muscle thickness at more than one or several specific locations.
As shown in Fig. 8 and Table II, for the results from a representative subject, PT provided a higher level of measurement stability with lower interframe difference. Given the fact that change of aponeuroses width in a sequence is trivial, the SD of thickness of superficial aponeurosis itself in a sequence could indirectly reflect the error level of our strategy.
In this paper, we present a new strategy for the measurement of muscle thickness along the entire length of aponeuroses in ultrasonography, which is useful when curvature of aponeuroses and longitudinal variance of muscle thickness are unnegligible. Results of the experiments suggest that the proposed strategy can be used for objective estimation of muscle thickness in musculoskeletal ultrasound images.
Authors: Shan Ling | Yongjin Zhou | Ye Chen | Yu-Qian Zhao | Lei Wang | Yong-Ping Zheng