Department of Electronics & Communication Engineering, National Institute of Technology Calicut, India.
Department of Electronics & Communication Engineering, National Institute of Technology Calicut, India.
Artif Intell Med. 2019 Mar;94:1-17. doi: 10.1016/j.artmed.2018.12.006. Epub 2018 Dec 31.
Low-dose Computed Tomography (CT) imaging is a most commonly used medical imaging modality. Though the reduction in dosage reduces the risk due to radiation, it leads to an increase in noise level. Hence, it is a mandatory requirement to include a noise reduction technique as a pre- and/or post-processing step for better disease diagnosis. The nuclear norm minimization has attracted a great deal of research interest in contemporary years. This paper proposes a low-rank approximation based approach for denoising of CT images by effectively utilizing the global spatial correlation and local smoothness properties. The tensor nuclear norm is used to describe the global properties and the tensor total variation is used to characterize the local smoothness as well as to improve global smoothness. The resulting optimization problem is solved by the Alternative Direction Method of Multipliers (ADMM) technique. Experimental results on simulated and real CT data prove that the proposed methods outperform the state-of-art works.
低剂量计算机断层扫描（CT）成像技术是一种最常用的医学成像方式。虽然剂量的减少降低了辐射风险，但它会导致噪声水平的增加。因此，作为更好地进行疾病诊断的预处理和/或后处理步骤，必须包括降噪技术。核范数最小化在近年来引起了大量的研究兴趣。本文提出了一种基于低秩逼近的 CT 图像去噪方法，通过有效利用全局空间相关性和局部平滑性来实现。张量核范数用于描述全局特性，张量全变差用于描述局部平滑性并提高全局平滑性。所得到的优化问题通过交替方向乘子法（ADMM）技术来解决。在模拟和真实 CT 数据上的实验结果表明，所提出的方法优于最先进的方法。
