eprintid: 28484 rev_number: 13 eprint_status: archive userid: 19147 dir: disk0/00/02/84/84 datestamp: 2019-01-16 08:32:15 lastmod: 2021-04-02 15:58:50 status_changed: 2019-01-16 08:32:15 type: article metadata_visibility: show creators_name: Ouzir, Nora creators_name: Basarab, Adrian creators_name: Liebgott, Hervé creators_name: Harbaoui, Brahim creators_name: Tourneret, Jean-Yves creators_idrefppn: 223312932 creators_idrefppn: 144148072 creators_idrefppn: 101396023 creators_idrefppn: 137452373 creators_idrefppn: 058595643 title: Motion Estimation in Echocardiography Using Sparse Representation and Dictionary Learning ispublished: pub subjects: subjects_INFO abstract: 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. date: 2018 date_type: published publisher: Institute of Electrical and Electronics Engineers id_number: 10.1109/TIP.2017.2753406 official_url: http://ieeexplore.ieee.org/document/8039230/ faculty: info divisions: IRIT keywords: Cardiac ultrasound - Dictionary learning - Motion estimation Sparse representations language: en has_fulltext: TRUE view_date_year: 2018 full_text_status: public publication: IEEE Transactions on Image Processing volume: 27 number: 1 pagerange: 64-77 refereed: TRUE issn: 1057-7149 harvester_local_overwrite: eprintid harvester_local_overwrite: userid harvester_local_overwrite: date harvester_local_overwrite: official_url harvester_local_overwrite: issn harvester_local_overwrite: dir harvester_local_overwrite: keywords harvester_local_overwrite: pagerange harvester_local_overwrite: publisher harvester_local_overwrite: volume harvester_local_overwrite: creators_name harvester_local_overwrite: faculty harvester_local_overwrite: site harvester_local_overwrite: abstract harvester_local_overwrite: title harvester_local_overwrite: publication harvester_local_overwrite: type harvester_local_overwrite: number harvester_local_overwrite: note harvester_local_overwrite: ispublished harvester_local_overwrite: id_number harvester_local_overwrite: event_title harvester_local_overwrite: pres_type harvester_local_overwrite: divisions harvester_local_overwrite: subjects harvester_local_overwrite: date_type harvester_local_overwrite: language harvester_local_overwrite: refereed harvester_local_overwrite: creators_idrefppn site: ut1 citation: 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. document_url: https://publications.ut-capitole.fr/id/eprint/28484/1/Ouzir_28484.pdf