Authors:
A. Martín
1
;
J. F. Garamendi
2
and
E. Schiavi
2
Affiliations:
1
Fundación CIEN-Fundación Reina Sofía, Spain
;
2
Universidad Rey Juan Carlos, Spain
Keyword(s):
MRI Rician Denoising, Total Variation, Numerical Resolution, ROF Model.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Medical Image Detection, Acquisition, Analysis and Processing
Abstract:
We consider a variational Rician denoising model for Magnetic Resonance Images (MRI) that we solve by a semi-implicit numerical scheme, which leads to the resolution of a sequence of Rudin, Osher and Fatemi (ROF) models. This allows to implement efficient numerical gradient descent schemes based on the dual formulation of the ROF model which are compared with a direct semi-implicit approach for the primal problem recently proposed for model validation. In this new framework the total variation operator is exactly solved as opposed to the approximating problems which must be considered when the primal problem is dealt with. The comparison among the above methods is performed using synthetic and real MR brain images and the results show the effectiveness of the new method in both, the accuracy and the speeding up of the algorithm.