Estimation of Muscle Fascicle Orientation in Ultrasonic Images
This work addresses the need for reliable muscle fascicle angle estimation in medical imaging, but it is incremental as it compares existing methods rather than introducing a new one.
The paper tackled the problem of automatically estimating muscle fascicle orientation in ultrasonic images by comparing four algorithms, finding that a combination of vesselness filter pre-processing and projection profile method achieved the best agreement with expert ground truth data on 425 image frames.
We compare four different algorithms for automatically estimating the muscle fascicle angle from ultrasonic images: the vesselness filter, the Radon transform, the projection profile method and the gray level cooccurence matrix (GLCM). The algorithm results are compared to ground truth data generated by three different experts on 425 image frames from two videos recorded during different types of motion. The best agreement with the ground truth data was achieved by a combination of pre-processing with a vesselness filter and measuring the angle with the projection profile method. The robustness of the estimation is increased by applying the algorithms to subregions with high gradients and performing a LOESS fit through these estimates.