RT Journal Article
SR 00
ID 10.1109/TIP.2017.2753406
A1 Ouzir, Nora
A1 Basarab, Adrian
A1 Liebgott, Hervé
A1 Harbaoui, Brahim
A1 Tourneret, Jean-Yves
T1 Motion Estimation in Echocardiography Using Sparse Representation and Dictionary Learning
JF IEEE Transactions on Image Processing
YR 2018
FD 2018
VO 27
IS 1
SP 64
OP 77
K1 Cardiac ultrasound - Dictionary learning - Motion estimation  Sparse representations
AB This paper introduces a new method for cardiac motion estimation in 2-D ultrasound images. The motion esti- mation problem is formulated as an energy minimization, whose data fidelity term is built using the assumption that the images are corrupted by multiplicative Rayleigh noise. In addition to a classical spatial smoothness constraint, the proposed method exploits the sparse properties of the cardiac motion to regularize the solution via an appropriate dictionary learning step. The proposed method is evaluated on one data set with available ground-truth, including four sequences of highly realistic sim- ulations. The approach is also validated on both healthy and pathological sequences of in vivo data. We evaluate the method in terms of motion estimation accuracy and strain errors and compare the performance with state-of-the-art algorithms. The results show that the proposed method gives competitive results for the considered data. Furthermore, the in vivo strain analysis demonstrates that meaningful clinical interpretation can be obtained from the estimated motion vectors.
PB Institute of Electrical and Electronics Engineers
SN 1057-7149
LK https://publications.ut-capitole.fr/id/eprint/28484/
UL http://ieeexplore.ieee.org/document/8039230/