Motion Estimation in Echocardiography Using Sparse Representation and Dictionary Learning - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue IEEE Transactions on Image Processing Année : 2017

Motion Estimation in Echocardiography Using Sparse Representation and Dictionary Learning

Résumé

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.
Fichier principal
Vignette du fichier
Ouzir_19378.pdf (3.51 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01677873 , version 1 (08-01-2018)

Identifiants

Citer

Nora Leïla Ouzir, Adrian Basarab, Hervé Liebgott, Brahim Harbaoui, Jean-Yves Tourneret. Motion Estimation in Echocardiography Using Sparse Representation and Dictionary Learning. IEEE Transactions on Image Processing, 2017, vol. 27 (n° 1), pp.64-77. ⟨10.1109/TIP.2017.2753406⟩. ⟨hal-01677873⟩
311 Consultations
276 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More