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Computational and Mathematical Methods in Medicine
Volume 2013 (2013), Article ID 921303, 10 pages
Research Article

GPU-Based Block-Wise Nonlocal Means Denoising for 3D Ultrasound Images

Department of Biomedical Engineering, School of Life Science and Technology, Key Laboratory of Image Processing and Intelligence Control of Education Ministry of China, Huazhong University of Science and Technology, No. 1037, Luoyu Road, Wuhan 430074, China

Received 8 May 2013; Revised 28 July 2013; Accepted 4 September 2013

Academic Editor: Tianye Niu

Copyright © 2013 Liu Li 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.


Speckle suppression plays an important role in improving ultrasound (US) image quality. While lots of algorithms have been proposed for 2D US image denoising with remarkable filtering quality, there is relatively less work done on 3D ultrasound speckle suppression, where the whole volume data rather than just one frame needs to be considered. Then, the most crucial problem with 3D US denoising is that the computational complexity increases tremendously. The nonlocal means (NLM) provides an effective method for speckle suppression in US images. In this paper, a programmable graphic-processor-unit- (GPU-) based fast NLM filter is proposed for 3D ultrasound speckle reduction. A Gamma distribution noise model, which is able to reliably capture image statistics for Log-compressed ultrasound images, was used for the 3D block-wise NLM filter on basis of Bayesian framework. The most significant aspect of our method was the adopting of powerful data-parallel computing capability of GPU to improve the overall efficiency. Experimental results demonstrate that the proposed method can enormously accelerate the algorithm.