文献类型：期刊文献

英文题名：Nonlocal Means-Based Denoising for Medical Images

作者：Lu, Ke[1];He, Ning[2];Li, Liang[2]

第一作者：Lu, Ke

通讯作者：Lu, K[1]

机构：[1]Chinese Acad Sci, Grad Univ, Coll Comp & Commun Engn, Beijing 100049, Peoples R China;[2]Beijing Union Univ, Sch Informat, Beijing 100101, Peoples R China

第一机构：Chinese Acad Sci, Grad Univ, Coll Comp & Commun Engn, Beijing 100049, Peoples R China

通讯机构：[1]corresponding author), Chinese Acad Sci, Grad Univ, Coll Comp & Commun Engn, Beijing 100049, Peoples R China.

年份：2012

卷号：2012

外文期刊名：COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE

收录：;EI(收录号：20214511121970);Scopus(收录号：2-s2.0-84863260910);WOS:【SCI-EXPANDED(收录号:WOS:000301268900001)】；

基金：This work was supported by the NSFC (Grant nos. 61103130, 61070120, 60982145); National Program on Key basic research Project (973 Programs) (Grant nos. 2010CB731804-1, 2011CB706901-4); Beijing Natural Science Foundation (Grant no. 4112021); Foundation of Beijing Educational Committee (no. KM201111417015); the opening project of Shanghai key laboratory of integrate administration technologies for information security (no. AGK2010005).

语种：英文

外文关键词：Image analysis - Image enhancement - Medical imaging - Pixels

摘要：Medical images often consist of low-contrast objects corrupted by random noise arising in the image acquisition process. Thus, image denoising is one of the fundamental tasks required by medical imaging analysis. Nonlocal means (NL-means) method provides a powerful framework for denoising. In this work, we investigate an adaptive denoising scheme based on the patch NL-means algorithm for medical imaging denoising. In contrast with the traditional NL-means algorithm, the proposed adaptive NL-means denoising scheme has three unique features. First, we use a restricted local neighbourhood where the true intensity for each noisy pixel is estimated from a set of selected neighbouring pixels to perform the denoising process. Second, the weights used are calculated thanks to the similarity between the patch to denoise and the other patches candidates. Finally, we apply the steering kernel to preserve the details of the images. The proposed method has been compared with similar state-of-art methods over synthetic and real clinical medical images showing an improved performance in all cases analyzed.