Cardiac Motion Estimation Using Convolutional Sparse Coding
Date:
Recommended citation: N. Diaz, A. Basarab, J. -Y. Tourneret and H. A. Fuentes. “Cardiac Motion Estimation Using Convolutional Sparse Coding,” 2019 27th European Signal Processing Conference (EUSIPCO), 2019, pp. 1-5, doi: 10.23919/EUSIPCO.2019.8903163. [Paper], [Link], [Slides].
This paper studies a new motion estimation method based on convolutional sparse coding. The motion estimation problem is formulated as the minimization of a cost function composed of a data fidelity term, a spatial smoothness constraint, and a regularization based on convolution sparse coding. We study the potential interest of using a convolutional dictionary instead of a standard dictionary using specific examples. Moreover, the proposed method is evaluated in terms of motion estimation accuracy and compared with state-of-the-art algorithms, showing its interest for cardiac motion estimation.
Cite
@INPROCEEDINGS{Diaz3:published,
author={N. {Diaz} and A. {Basarab} and J.Y. {Tourneret} and H. {Arguello}},
booktitle={2019 27th European Signal Processing Conference (EUSIPCO), Coru\~na, Espa\~na},
title={Cardiac Motion Estimation Using Convolutional Sparse Coding},
year={2019},
volume={},
number={},
pages={},
keywords={Ultrasound imaging; cardiac motion estimation; Convolutional dictionary; sparse representation},
doi={10.1109/EUSIPCO.2019.7760641},
ISSN={},
month={Sep},}