A review on CT image noise and its denoising

被引:247
作者
Diwakar, Manoj [1 ]
Kumar, Manoj [2 ]
机构
[1] Uttaranchal Univ, UIT, Dept Comp Sci & Engn, Dehra Dun, Uttar Pradesh, India
[2] Babasaheb Bhimrao Ambedkar Univ, Dept Comp Sci, Lucknow, Uttar Pradesh, India
关键词
Computed Tomography; Image denoising; Anisotropic function; Isotropic function; Total variation; TOTAL VARIATION MODEL; LOW-DOSE CT; ITERATIVE RECONSTRUCTION; COMPUTED-TOMOGRAPHY; WAVELET SHRINKAGE; EDGE-DETECTION; ALGORITHMS; REDUCTION; QUALITY; REGULARIZATION;
D O I
10.1016/j.bspc.2018.01.010
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
CT imaging is widely used in medical science over the last decades. The process of CT image reconstruction depends on many physical measurements such as radiation dose, software/hardware. Due to statistical uncertainty in all physical measurements in Computed Tomography, the inevitable noise is introduced in CT images. Therefore, edge-preserving denoising methods are required to enhance the quality of CT images. However, there is a tradeoff between noise reduction and the preservation of actual medical relevant contents. Reducing the noise without losing the important features of the image such as edges, corners and other sharp structures, is a challenging task. Nevertheless, various techniques have been presented to suppress the noise from the CT scanned images. Each technique has their own assumptions, merits and limitations. This paper contains a survey of some significant work in the area of CT image denoising. Often, researchers face difficulty to understand the noise in CT images and also to select an appropriate denoising method that is specific to their purpose. Hence, a brief introduction about CT imaging, the characteristics of noise in CT images and the popular methods of CT image denoising are presented here. The merits and drawbacks of CT image denoising methods are also discussed. (C) 2018 Elsevier Ltd. All rights reserved.
引用
收藏
页码:73 / 88
页数:16
相关论文
共 145 条
  • [1] Adaptive Tensor-Based Principal Component Analysis for Low-Dose CT Image Denoising
    Ai, Danni
    Yang, Jian
    Fan, Jingfan
    Cong, Weijian
    Wang, Yongtian
    [J]. PLOS ONE, 2015, 10 (05):
  • [2] Al-Ameen Z., 2014, COMPUTER METHODS BIO, P1
  • [3] Attenuating Noise from Computed Tomography Medical Images using a Coefficients-Driven Total Variation Denoising Algorithm
    Al-Ameen, Zohair
    Sulong, Ghazali
    [J]. INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, 2014, 24 (04) : 350 - 358
  • [4] [Anonymous], 2017, ARXIV170200288
  • [5] [Anonymous], 2016, ARXIV161009736
  • [6] [Anonymous], 2013, P NUCL SCI S MED IM
  • [7] [Anonymous], 2004, P GSPX
  • [8] [Anonymous], 1998, ANISOTROPIC DIFFUSIO
  • [9] [Anonymous], 2006, Digital Image Processing
  • [10] [Anonymous], 2009, Computed tomography: principles, design, artifacts, and recent advances