MRI denoising using non-local means.
Magnetic Resonance (MR) images are affected by random noise which limits the accuracy of any quantitative measurements from the data. In the present work, a recently proposed filter for random noise removal is analyzed and adapted to reduce this noise in MR magnitude images. This parametric filter, named Non-Local Means (NLM), is highly dependent on the setting of its parameters. The aim of this paper is to find the optimal parameter selection for MR magnitude image denoising. For this purpose, experiments have been conducted to find the optimum parameters for different noise levels. Besides, the filter has been adapted to fit with specific characteristics of the noise in MR image magnitude images (i.e. Rician noise). From the results over synthetic and real images we can conclude that this filter can be successfully used for automatic MR denoising.
Manjón
José V
JV
Biomedical Informatics Group (IBIME), ITACA Institute, Polytechnic University of Valencia, Camino de Vera, s/n. 46022 Valencia, Spain.
Carbonell-Caballero
José
J
Biomedical Informatics Group (IBIME), ITACA Institute, Polytechnic University of Valencia, Camino de Vera, s/n. 46022 Valencia, Spain.
Lull
Juan J
JJ
Biomedical Informatics Group (IBIME), ITACA Institute, Polytechnic University of Valencia, Camino de Vera, s/n. 46022 Valencia, Spain.
García-Martí
Gracián
G
Biomedical Informatics Group (IBIME), ITACA Institute, Polytechnic University of Valencia, Camino de Vera, s/n. 46022 Valencia, Spain.
Martí-Bonmatí
Luís
L
Department of Radiology, Dr. Peset Hospital and Quirón Hospital, Valencia, Spain.
Robles
Montserrat
M
Biomedical Informatics Group (IBIME), ITACA Institute, Polytechnic University of Valencia, Camino de Vera, s/n. 46022 Valencia, Spain.
D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D013131:Spine / Q000033:anatomy & histology