MRI denoising using Non-Local Means

被引：426

作者：

Manjon, Jose V. ^{[1
]}

Carbonell-Caballero, Jose ^{[1
]}

Lull, Juan J. ^{[1
]}

Garcia-Marti, Gracian ^{[1
]}

Marti-Bonmati, Luis ^{[2
,3
]}

Robles, Montserrat ^{[1
]}

机构：

[1] Univ Politecn Valencia, ITACA Inst, Biomed Informat Grp IBIME, Valencia 46022, Spain

[2] Dr Peset Hosp, Dept Radiol, Valencia, Spain

[3] Quiron Hosp, Valencia, Spain

来源：

MEDICAL IMAGE ANALYSIS | 2008年 / 12卷 / 04期

关键词：

MRI filtering; random noise; denoising;

D O I：

10.1016/j.media.2008.02.004

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 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 image denoising. (C) 2008 Elsevier B.V. All rights reserved.

引用

页码：514 / 523

页数：10

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