An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images
An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images
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DOI：
10.1109/tmi.2007.906087
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发表时间：
2008-04-01
影响因子：
10.6
通讯作者：
Barillot, Christian
中科院分区：
文献类型：
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作者：
Coupe, Pierrick;Yger, Pierre;Barillot, Christian
图像复原中的一个关键问题是如何在去除噪声的同时保持相关图像信息的完整性。去噪是提高图像质量和改善定量成像分析所需所有任务的性能的关键一步。本文提出的方法是基于三维优化的非局部(NL)-均值滤波器(Buade，et al.，2005)。NL-Means滤波器利用待学习图像中信息的冗余度来去除噪声。对于二维图像，NI.-Means滤波的性能已经得到了验证，但减少计算负担是将该方法推广到三维图像的一个关键方面。为了克服这一问题，我们提出了降低计算复杂度的改进方案。这些不同的改进允许大幅划分计算时间，同时保持NL-Means滤波器的性能。然后提出了NL-Means过滤器的全自动化和优化版本。我们对NL-Means过滤器的贡献是：1)平滑参数的自动调整；2)最相关体素的选择；3)分块实现；以及4)并行计算。在用BrainWeb生成的合成数据集上进行了定量验证(Collins等人，1998)。结果表明，我们的优化NL-Means滤波器在计算时间较短的情况下，在精度(以峰值信噪比衡量)方面优于NL-Means滤波器的经典实现，以及另外两种经典的去噪方法[各向异性扩散法(Perona and Malik，1990)]和总变差最小化方法(TVS，1992)。最后，给出了实际数据的定性结果。
A critical issue in image restoration is the problem of noise removal while keeping the integrity of relevant image information. Denoising is a crucial step to increase image quality and to improve the performance of all the tasks needed for quantitative imaging analysis. The method proposed in this paper is based on a 3-D optimized blockwise version of the nonlocal (NL)-means filter (Buades, et al., 2005). The NL-means filter uses the redundancy of information in the image under study to remove the noise. The performance of the NI.-means filter has been already demonstrated for 2-D images, but reducing the computational burden is a critical aspect to extend the method to 3-D images. To overcome this problem, we propose improvements to reduce the computational complexity. These different improvements allow to drastically divide the computational time while preserving the performances of the NL-means filter. A fully automated and optimized version of the NL-means filter is then presented. Our contributions to the NL-means filter are: 1) an automatic tuning of the smoothing parameter; 2) a selection of the most relevant voxels; 3) a blockwise implementation; and 4) a parallelized computation. Quantitative validation was carried out on synthetic datasets generated with BrainWeb (Collins, et al., 1998). The results show that our optimized NL-means filter outperforms the classical implementation of the NL-means filter, as well as two other classical denoising methods [anisotropic diffusion (Perona and Malik, 1990)] and total variation minimization process (Rudin, et al., 1992) In terms of accuracy (measured by the peak signal-to-noise ratio) with low computation time. Finally, qualitative results on real data are presented.