Dynamic measurement of pennation angle of gastrocnemius muscles during contractions based on ultrasound imaging
© Zhou et al.; licensee BioMed Central Ltd. 2012
Received: 8 August 2012
Accepted: 28 August 2012
Published: 3 September 2012
Muscle fascicle pennation angle (PA) is an important parameter related to musculoskeletal functions, and ultrasound imaging has been widely used for measuring PA, but manually and frame by frame in most cases. We have earlier reported an automatic method to estimate aponeurosis orientation based on Gabor transform and Revoting Hough Transform (RVHT).
In this paper, we proposed a method to estimate the overall orientation of muscle fascicles in a region of interest, in order to complete computing the orientation of the other side of the pennation angle, but the side found by RVHT. The measurements for orientations of both fascicles and aponeurosis were conducted in each frame of ultrasound images, and then the dynamic change of pennation angle during muscle contraction was obtained automatically. The method for fascicle orientation estimation was evaluated using synthetic images with different noise levels and later on 500 ultrasound images of human gastrocnemius muscles during isometric plantarflexion.
The muscle fascicle orientations were also estimated manually by two operators. From the results it’s found that the proposed automatic method demonstrated a comparable performance to the manual method.
With the proposed methods, ultrasound measurement for muscle pennation angles can be more widely used for functional assessment of muscles.
KeywordsUltrasound image Pennation angle Hough transform Sonomyography SMG Electromyography EMG Gastrocnemius muscle Orientation
Muscle fascicle pennation angle (PA), muscle thickness (MT) and fiber length (FL) and their dynamic changes during muscle contraction have become important measures for skeletal muscle studies using ultrasound, for example [1–5]. The change of PA and FL over the time can form signals, representing architectural muscle behavior under contraction, similar to the change of MT, which has been defined as sonomyography (SMG) . SMG can provide muscle functional information complementary to electromyography (EMG) and torque signals [7, 8]. In previous studies, pennation angles were conventionally detected manually in ultrasound images of muscles, for example [2, 9–11], and this greatly affects the wider applications of this parameter, particularly for the study of dynamic muscle contraction [12–14].
Recently, a number of studies have been reported for the automatic estimation of muscle fascicle orientation and pennation angle using revoting Hough transform (RVHT) [15, 16], Radon transform [17, 18] or features-separability filtering .
Estimation of deep aponeurosis orientation
The estimation of the deep aponeurosis orientation (O2 in Figure 1) was based on the methods that we have reported earlier, which included using Gabor filtering to enhance ultrasound images and RVHT to estimate the orientation [15, 16]. This process was repeated for each frame of ultrasound images, and the orientation change was recorded using RVHT method [15, 16].
Estimation of dominant fascicle orientation of selected region
where is the least square estimation of the orientation at pixel and w x w defined its neighborhood area involved.
where Γ is a small neighboring region of the pixel, and its size is related to the local frequency of strongly oriented patterns. The reliability coefficient here is a number between 0 and 1, and its two extremities, 0 and 1, correspond to the isotropic region and the strongly oriented pattern, respectively. To estimate the dominant orientation of the selected region, we used the median value of the orientations for each pixel in the region, as long as its reliability coefficient was larger than an empirically pre-defined threshold, 0.6 in this paper. The fascicle pennation angle was computed as the difference between the dominant orientation of the selected fascicle region O1 and the deep aponeurosis orientation O2.
Estimation of the dynamic changes of pennation angle
Evaluation of the texture dominant orientation method using synthetic images
Evaluation of the automatic estimation method for continuous pennation angle changes
Surface EMG signals were collected from the gastrocnemius muscle using bipolar Ag-AgCl electrodes (Axon System, Inc., NY, USA), amplified by a multiple channel amplifier (RM6280 Multi-Channel Biosignal Collection and Processing System, Chengdu Instrument Company, Chengdu, China), with a gain of 2000, filtered separately by 10–400 Hz, 5-100 Hz band-pass analog filters within the amplifier, and then digitized by a 12-bit data acquisition card (NI-DAQ 6024E, National Instruments Corporation, Austin, TX, USA) with a sampling rate of 1 kHz. Ultrasound image sequences, surface EMG and torque signals were simultaneously collected and stored by a custom-made program for ultrasonic measurement of motion and elasticity (UMME, http://www.tups.org).
One young male subject (age 29, body weight 67Kg and height 172 cm) participated in the test to demonstrate the feasibility of the method. Human subject ethical approval was obtained from the relevant committee in the authors’ institution, and informed consent was obtained from the subject prior to the experiment. The testing position of the subject was in accordance with the Humac/Norm User’s Guide. The subject was instructed to put forth his maximal effort of isometric plantarflexion for a period of 3 s with verbal encouragement provided. The maximal voluntary contraction (MVC) was defined as the highest value of torque recorded during the entire isometric contraction. A rest of 5 min was allowed before the subject performing another MVC test. The MVC torque was then calculated by averaging the two recorded highest torque values from the two tests. The subject was instructed to generate a torque waveform in rough sinusoid shape, up to 90% of his MVC, using ankle plantarflexion movements in prone position.
In this study, we separated the process for automatic estimation of the pennation angle into two steps, including the estimation of orientations of the deep aponeurosis and the muscle fascicles in a selected representative region. The muscle pennation angle can be obtained using the difference between these two orientations. A procedure was proposed for automatic measurement of pennation angle in a sequence of ultrasound images of muscles. Using the synthetic images with fascicle-like patterns with various noise levels, we demonstrated that the proposed fascicle orientation estimation method is robust.
In the estimation of the dominant orientation of the fascicles in the selected region of interested, we proposed to use the reliability of orientation field  to rule out contributions from regions where the texture orientation is not reliable or in other words, where the pattern appears more isotropic than oriented. The reliability coefficient ranges from 0 to 1, with 0 representing an isotropic region and 1 representing a strongly oriented pattern. When the reliability coefficient is smaller than a certain threshold, the calculated orientation is regarded as not reliable and should be neglected. In this study, we selected a threshold of 0.6, which was determined after many trials. Whether this value is applicable for ultrasound images from different muscles with different image qualities should be further investigated in the future.
It was noted that the original signal about the pennation angle changes detected using the proposed method was not as smooth as the torque signal, but was similar to the EMG RMS signal (Figure 7). This noisy feature was also observed in the pennation angle changes detected manually (Figure 5). The reason for such “noises” overlapped with the signals about the pennation angle changes was not clear, and future studies are required to better understand whether such “noises” are caused by intrinsic properties of muscle during contraction or by calculation errors. We have also demonstrated that the signal could be processed to become smoother. Using the original pennation angle changes, we compared the results obtained by the automatic method and the manual method, using the mean of the results obtained by the two operators. The results showed a good agreement (Figure 6) between the results by the proposed automatic method and the averaged manual estimation. Similar results have been reported previously [17, 23]. The automatic method proposed in this paper may help solve problems of subjectivity and inconsistency caused by the conventional manual measurement, in addition to reduce the processing time.
In summary, we have successfully developed a method for automatic measurement of muscle pennation angle in a series of ultrasound images of muscles. The preliminary results demonstrated the measurement was reliable. Further studies are required to test whether this new method is applicable for ultrasound images of different muscles under different contractions. Since the pennation angle can be easily obtained automatically using this new method, we think this important muscle parameter can be used more widely for the functional assessment of muscles.
The project is supported partially by the Hong Kong Innovation and Technology Commission (GHP/047/09), the National 863 Program of China (2012AA02A604), the National 973 Program of China (2010CB732606), the next generation communication technology Major project of National S&T (2013ZX03005013), the ‘Low-cost Healthcare’ Programs of Chinese Academy of Sciences, Guangdong Innovation Research Team Fund for Low-cost Healthcare Technologies (GIRTF-LCHT), the Guangdong Innovative Research Team Program (2011S013), International Science and Technology Cooperation Program of Guangdong Province (2012B050200004) and the Shenzhen Key Laboratory for Low-cost Healthcare (CXB201005260056A).
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