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首页> 外文期刊>Medical image analysis >Denoising of 3D magnetic resonance images by using higher-order singular value decomposition
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Denoising of 3D magnetic resonance images by using higher-order singular value decomposition

机译：通过使用高阶奇异值分解对3D磁共振图像进行去噪

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摘要

The denoising of magnetic resonance (MR) images is important to improve the inspection quality and reliability of quantitative image analysis. Nonlocal filters by exploiting similarity and/or sparseness among patches or cubes achieve excellent performance in denoising MR images. Recently, higher-order singular value decomposition (HOSVD) has been demonstrated to be a simple and effective method for exploiting redundancy in the 3D stack of similar patches during denoising 2D natural image. This work aims to investigate the application and improvement of HOSVD to denoising MR volume data. The wiener-augmented HOSVD method achieves comparable performance to that of BM4D. For further improvement, we propose to augment the standard HOSVD stage by a second recursive stage, which is a repeated HOSVD filtering of the weighted summation of the residual and denoised image in the first stage. The appropriate weights have been investigated by experiments with different image types and noise levels. Experimental results over synthetic and real 3D MR data demonstrate that the proposed method outperforms current state-of-the-art denoising methods. (C) 2014 Elsevier B.V. All rights reserved.
机译：磁共振（MR）图像的去噪对提高定量图像分析的检查质量和可靠性很重要。通过利用补丁或立方体之间的相似性和/或稀疏性，非局部滤波器在对MR图像进行去噪方面具有出色的性能。最近，高阶奇异值分解（HOSVD）已被证明是在对2D自然图像进行去噪期间利用相似补丁的3D堆栈中的冗余的一种简单有效的方法。这项工作旨在研究HOSVD在去噪MR体积数据方面的应用和改进。维纳增强型HOSVD方法可实现与BM4D相当的性能。为了进一步改进，我们建议通过第二个递归阶段来扩展标准HOSVD阶段，这是在第一阶段对残差和去噪图像的加权总和进行重复的HOSVD滤波。通过使用不同图像类型和噪声水平的实验研究了合适的权重。在合成和真实3D MR数据上的实验结果表明，所提出的方法优于当前的最新去噪方法。 （C）2014 Elsevier B.V.保留所有权利。

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