Kawamoto Shota, Imamoglu Nevrez, Gomez-Tames Jose D, Kita Kahori, Yu Wenwei
Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:250-3. doi: 10.1109/EMBC.2014.6943576.
Ultrasound imaging is an effective way to measure the muscle activity in electrical stimulation studies. However, it is a time consuming task to manually measure pennation angle and muscle thickness, which are the benchmark features to analyze muscle activity from the ultrasound imaging. In previous studies, the muscle features were measured by calculating optical flow of the pennation angle by using only fibers of a muscle from the ultrasound, without carefully considering moving muscle edges during active and passive contraction. Therefore, this study aimed to measure the pennation angle and muscle thickness by using the edges and fibers of a muscle in a quantitative way in a semi-automatic optical flow based approach. The results of the semi-automatic analysis were compared to that of manual measurement. Through the comparison, it is clear that the proposed algorithm could achieve higher accuracy in tracking the thickness and pennation angle for a sequence of ultrasound images.
超声成像在电刺激研究中是测量肌肉活动的有效方法。然而，手动测量羽状角和肌肉厚度是一项耗时的任务，而羽状角和肌肉厚度是从超声成像分析肌肉活动的基准特征。在以往的研究中，仅通过超声中一块肌肉的纤维来计算羽状角的光流来测量肌肉特征，而没有仔细考虑主动和被动收缩过程中移动的肌肉边缘。因此，本研究旨在通过基于半自动光流的方法，以定量方式利用肌肉的边缘和纤维来测量羽状角和肌肉厚度。将半自动分析的结果与手动测量的结果进行比较。通过比较，很明显所提出的算法在跟踪一系列超声图像的厚度和羽状角方面可以实现更高的准确性。
