An automatic muscle fiber orientation tracking algorithm using Bayesian Kalman Filter for ultrasound images

In this study, an automatic muscle fiber orientation tracking approach based on Bayesian Kalman Filter (BKF) is proposed. The BKF employs a Gaussian mixture (GM) representation of the state and noise densities and a novel direct density simplifying algorithm for avoiding the exponential complexity growth of conventional Kalman filters (KFs) using GM. In this paper, the ultrasound image is firstly enhanced by a bank of Gabor Filters (GFs) based on the GM of the state density in BKF. Then, a bank of localized radon transforms (LRTs) are used to extract muscle fiber orientations and the dominant orientation is obtained by minimizing an energy function. Finally, the dominant orientation is fed back to the BKF as an observation. The performance of the proposed approach is compared with existing methods on five subjects over 1000+ clinical ultrasound images. Experimental results show that the proposed method can achieve accurate and robust measurements of fascicle orientation and outperforms all the existing methods.