Denoising MR images using non-local means filter with combined patch and pixel similarity.
Denoising MR images using non-local means filter with combined patch and pixel similarity.
复制标题
使用结合块和像素相似性的非局部均值滤波器对 MR 图像进行去噪
DOI：
10.1371/journal.pone.0100240
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Feng Y
中科院分区：
文献类型：
--
作者：
Zhang X;Hou G;Ma J;Yang W;Lin B;Xu Y;Chen W;Feng Y
当以低信噪比采集磁共振 (MR) 图像时，去噪对于提高视觉质量和关联定量分析的可靠性至关重要。经典的非局部均值 (NLM) 滤波器通过邻域相似性对像素进行平均加权，经过调整和证明可以有效减少莱斯噪声，而不影响 MR 幅度图像中的边缘细节。然而，莱斯 NLM (RNLM) 滤波器通常会模糊小的高对比度颗粒细节，而这些细节可能是临床相关信息。在本文中，我们研究了这种粒子模糊问题的原因，并提出了一种具有组合补丁和像素（RNLM-CPP）相似性的新型粒子保留 RNLM 滤波器。对合成和真实 MR 数据的实验结果表明，所提出的 RNLM-CPP 滤波器在对 MR 图像进行去噪时可以比原始 RNLM 滤波器更好地保留小高对比度粒子细节。
Denoising is critical for improving visual quality and reliability of associative quantitative analysis when magnetic resonance (MR) images are acquired with low signal-to-noise ratios. The classical non-local means (NLM) filter, which averages pixels weighted by the similarity of their neighborhoods, is adapted and demonstrated to effectively reduce Rician noise without affecting edge details in MR magnitude images. However, the Rician NLM (RNLM) filter usually blurs small high-contrast particle details which might be clinically relevant information. In this paper, we investigated the reason of this particle blurring problem and proposed a novel particle-preserving RNLM filter with combined patch and pixel (RNLM-CPP) similarity. The results of experiments on both synthetic and real MR data demonstrate that the proposed RNLM-CPP filter can preserve small high-contrast particle details better than the original RNLM filter while denoising MR images.
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