Muscular motion estimation from 4-D ultrasound image using Kalman filter and rotation-invariant feature descriptor

Muscular motion estimation in ultrasound images is of great importance for investigating causes of musculoskeletal conditions in pathological examinations. However, the quality of ultrasound images is usually depressed due to speckle noises and temporal decorrelation of speckle patterns, making certain difficulties in motion estimation. To resolve the problem, this paper presents a new model-based tracking method for estimating the perimysium motion from 4-D ultrasound images. From the first frame of the given motion images, the proposed method builds a perimysium model, which consists of 3-D surface and rotation-invariant feature descriptor (RIFD) to characterize its structural and image appearances. Then, the model is applied to the next frame using Kalman filter for estimating the best matching position with the highest similarity of RIFD. The estimation is used to update the motion state for predicting and refining the model position in the next frame. The Kalman filtering is iteratively performed until the entire image sequence is processed. Overall, the proposed method efficiently combines the structure, image and motion priors, so it can overcome the aforementioned difficulties. Experimental results showed that the proposed method can provide reliable and accurate estimation of perimysium motion with tracking errors 6.26 voxels using three 4-D ultrasound volumes.