TY - GEN
T1 - Majorization-minimization algorithms for maximum likelihood estimation of magnetic resonance images
AU - Jiang, Qianyi
AU - Moussaoui, Saïd
AU - Idier, Jérǒme
AU - Collewet, Guylaine
AU - Xu, Mai
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - This paper addresses maximum likelihood estimation of images corrupted by a Rician noise, with the aim to propose an efficient optimization method. The application example is the restoration of magnetic resonance images. Starting from the fact that the criterion to minimize is non-convex but unimodal, the main contribution of this work is to propose an optimization scheme based on the majorization-minimization framework after introducing a variable change allowing to get a strictly convex criterion. The resulting descent algorithm is compared to the classical MM descent algorithm and its performances are assessed using synthetic and real MR images. Finally, by combining these two MM algorithms, two optimization strategies are proposed to improve the numerical efficiency of the image restoration for any signal-to-noise ratio.
AB - This paper addresses maximum likelihood estimation of images corrupted by a Rician noise, with the aim to propose an efficient optimization method. The application example is the restoration of magnetic resonance images. Starting from the fact that the criterion to minimize is non-convex but unimodal, the main contribution of this work is to propose an optimization scheme based on the majorization-minimization framework after introducing a variable change allowing to get a strictly convex criterion. The resulting descent algorithm is compared to the classical MM descent algorithm and its performances are assessed using synthetic and real MR images. Finally, by combining these two MM algorithms, two optimization strategies are proposed to improve the numerical efficiency of the image restoration for any signal-to-noise ratio.
KW - Magnetic resonance imaging
KW - Rician noise
KW - iterative optimization
KW - majorization-minimization
KW - maximum likelihood estimation
UR - https://www.scopus.com/pages/publications/85044759799
U2 - 10.1109/IPTA.2017.8310150
DO - 10.1109/IPTA.2017.8310150
M3 - 会议稿件
AN - SCOPUS:85044759799
T3 - Proceedings of the 7th International Conference on Image Processing Theory, Tools and Applications, IPTA 2017
SP - 1
EP - 6
BT - Proceedings of the 7th International Conference on Image Processing Theory, Tools and Applications, IPTA 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Conference on Image Processing Theory, Tools and Applications, IPTA 2017
Y2 - 28 November 2017 through 1 December 2017
ER -