Computational and Mathematical Methods in Medicine
Volume 2012 (2012), Article ID 438617, 7 pages
Research Article

Nonlocal Means-Based Denoising for Medical Images

1College of Computing & Communication Engineering, Graduate University of Chinese Academy of Sciences, Beijing 100049, China
2School of Information, Beijing Union University, Beijing 100101, China

Received 20 October 2011; Accepted 29 November 2011

Academic Editor: Sheng-yong Chen

Copyright © 2012 Ke Lu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.