Denoising Magnetic Resonance Images Using Collaborative Non-Local Means.
Denoising Magnetic Resonance Images Using Collaborative Non-Local Means.
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DOI：
10.1016/j.neucom.2015.11.031
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发表时间：
2016-02-12
期刊：
影响因子：
6
通讯作者：
Yap PT
中科院分区：
文献类型：
--
作者：
Chen G;Zhang P;Wu Y;Shen D;Yap PT
磁共振（MR）图像中的噪声伪影增加了图像处理工作流的复杂性，并且降低了从图像得出的推断的可靠性。因此，通常期望预先去除这样的伪影以进行更鲁棒和有效的定量分析。在去除MR图像中的噪声的同时，保持相关图像信息的完整性是重要的。已经为此目的开发了各种方法，并且非局部均值（NLM）滤波器已被证明能够实现最先进的去噪性能。为了有效地去噪，NLM在很大程度上依赖于重复结构模式的存在，然而，这可能并不总是存在于单个图像中。当人们考虑到人类大脑是复杂的并且包含许多独特结构的事实时，这一点尤其正确。在本文中，我们提出利用重复结构从多个图像协同去噪的图像。潜在的假设是，从多次扫描比从单次扫描更有可能找到重复结构。具体地，为了对目标图像进行去噪，可以从不同对象获取的多个图像在空间上与目标图像对准，并且以目标图像作为基准对这些对准的图像执行类NLM块匹配。这将显著增加匹配结构的数量，从而提高去噪性能。在合成数据和真实的数据上的实验表明，该方法，协作非局部均值（CNLM），优于经典的NLM和产量的结果与显着改善的结构细节。
Noise artifacts in magnetic resonance (MR) images increase the complexity of image processing workflows and decrease the reliability of inferences drawn from the images. It is thus often desirable to remove such artifacts beforehand for more robust and effective quantitative analysis. It is important to preserve the integrity of relevant image information while removing noise in MR images. A variety of approaches have been developed for this purpose, and the non-local means (NLM) filter has been shown to be able to achieve state-of-the-art denoising performance. For effective denoising, NLM relies heavily on the existence of repeating structural patterns, which however might not always be present within a single image. This is especially true when one considers the fact that the human brain is complex and contains a lot of unique structures. In this paper we propose to leverage the repeating structures from multiple images to collaboratively denoise an image. The underlying assumption is that it is more likely to find repeating structures from multiple scans than from a single scan. Specifically, to denoise a target image, multiple images, which may be acquired from different subjects, are spatially aligned to the target image, and an NLM-like block matching is performed on these aligned images with the target image as the reference. This will significantly increase the number of matching structures and thus boost the denoising performance. Experiments on both synthetic and real data show that the proposed approach, collaborative non-local means (CNLM), outperforms the classic NLM and yields results with markedly improved structural details.