Automatic Tracking of Muscle Fiber Direction in Ultrasound Images Based on Improved Kalman Filter

Abstract

Ultrasound myograph (SMG) is a real-time and dynamic acquisition of muscle structure parameter changes by recording ultrasound images of muscle contraction through an ultrasound instrument. Muscle parameters are essential for judging whether the muscle and the human body are healthy. In order to solve the problem of muscle fiber tracking in a sequence of ultrasound muscle images, we propose a method to track the direction of muscle fibers automatically based on the improved Kalman filter. Firstly, the measurement value of the muscle fiber direction is obtained by introducing a reference line into the ultrasound muscle image based on deep learning. Secondly, the framework of a Kalman filter is improved by introducing a set of neural units. Finally, the optimal estimated value of muscle fiber direction is obtained by combining the measured value with the improved Kalman filter. It is verified by conducting experiments where the result obtained by our proposed method is closer to the manually labeled value compared with the original measurement method, and the root mean square error is reduced by about 10%.