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首页> 外文期刊>Image Processing, IET >Adaptive multiresolution non-local means filter for three-dimensional magnetic resonance image denoising
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Adaptive multiresolution non-local means filter for three-dimensional magnetic resonance image denoising

机译：三维磁共振图像去噪的自适应多分辨率非局部均值滤波器

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摘要

In this study, an adaptive multiresolution version of the blockwise non-local (NL)-means filter is presented for threedimensional (3D) magnetic resonance (MR) images. On the basis of an adaptive soft wavelet coefficient mixing, the proposed filter implicitly adapts the amount of denoising according to the spatial and frequency information contained in the image. Two versions of the filter are described for Gaussian and Rician noise. Quantitative validation was carried out on BrainWeb datasets by using several quality metrics. The results show that the proposed multiresolution filter obtained competitive performance compared with recently proposed Rician NL-means filters. Finally, qualitative experiments on anatomical and diffusion-weighted MR images show that the proposed filter efficiently removes noise while preserving fine structures in classical and very noisy cases. The impact of the proposed denoising method on fibre tracking is also presented on a HARDI dataset.
机译：在这项研究中，提出了针对三维（3D）磁共振（MR）图像的块状非局部（NL）均值滤波器的自适应多分辨率版本。在自适应软小波系数混合的基础上，所提出的滤波器根据图像中包含的空间和频率信息隐式地调整去噪量。描述了针对高斯和里斯噪声的两种滤波器版本。使用多个质量指标对BrainWeb数据集进行了定量验证。结果表明，与最近提出的Rician NL-means滤波器相比，提出的多分辨率滤波器获得了竞争性能。最后，对解剖和扩散加权MR图像的定性实验表明，在经典和非常嘈杂的情况下，所提出的滤波器可有效去除噪声，同时保留精细的结构。提出的降噪方法对光纤跟踪的影响也显示在HARDI数据集上。

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