Ouzir, Nora, Basarab, Adrian, Liebgott, Hervé, Harbaoui, Brahim and Tourneret, Jean-Yves (2018) Motion Estimation in Echocardiography Using Sparse Representation and Dictionary Learning. IEEE Transactions on Image Processing, 27 (1). pp. 64-77.

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Identification Number : 10.1109/TIP.2017.2753406


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.

Item Type: Article
Language: English
Date: 2018
Refereed: Yes
Uncontrolled Keywords: Cardiac ultrasound - Dictionary learning - Motion estimation Sparse representations
Divisions: Institut de Recherche en Informatique de Toulouse
Site: UT1
Date Deposited: 16 Jan 2019 08:32
Last Modified: 02 Apr 2021 15:58
URI: https://publications.ut-capitole.fr/id/eprint/28484
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