Motion Estimation in Echocardiography Using Sparse Representation and Dictionary Learning

Nora Ouzir, Adrian Basarab, Herve Liebgott, Brahim Harbaoui, Jean-Yves Tourneret
2018 IEEE Transactions on Image Processing  
OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : Eprints ID : 19378 To link to this article : Abstract-This paper introduces a new method for cardiac motion estimation in 2-D ultrasound images. The motion estimation problem is formulated as an energy minimization, whose data fidelity term is built using the assumption that
more » ... he 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 simulations. 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.
doi:10.1109/tip.2017.2753406 pmid:28922120 fatcat:r66b2qy235dkdciks5urifryza