A review on CT image noise and its denoising

被引:246
|
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 50 条
  • [31] From Noise Modeling to Blind Image Denoising
    Zhu, Fengyuan
    Chen, Guangyong
    Heng, Pheng Ann
    2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, : 420 - 429
  • [33] Hyperspectral Image Denoising by Asymmetric Noise Modeling
    Xu, Shuang
    Cao, Xiangyong
    Peng, Jiangjun
    Ke, Qiao
    Ma, Cong
    Meng, Deyu
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 60
  • [34] Image denoising for signal-dependent noise
    Hirakawa, K
    Parks, TW
    2005 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS 1-5: SPEECH PROCESSING, 2005, : 29 - 32
  • [35] LAN: Learning to Adapt Noise for Image Denoising
    Kim, Changjin
    Kim, Tae Hyun
    Baik, Sungyong
    2024 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2024, : 25193 - 25202
  • [36] Denoising an Image by Denoising Its Components in a Moving Frame
    Ghimpeteanu, Gabriela
    Batard, Thomas
    Bertalmio, Marcelo
    Levine, Stacey
    IMAGE AND SIGNAL PROCESSING, ICISP 2014, 2014, 8509 : 375 - 383
  • [37] Self-Augmented Noisy Image for Noise2Noise Image Denoising
    Limsuebchuea, Asavaron
    Duangsoithong, Rakkrit
    Phukpattaranont, Pornchai
    IEEE ACCESS, 2024, 12 : 71076 - 71087
  • [38] Adaptive noise-aware denoising network: Effective denoising for CT images with varying noise intensity
    Jin, Haoyang
    Tang, Yufei
    Liao, Feiyang
    Du, Qiang
    Wu, Zhongyi
    Li, Ming
    Zheng, Jian
    BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2024, 96
  • [39] Study of CT image denoising for image crossover feature confrontation
    Shi, Rui
    Li, Ying
    Ma, Chunmei
    Chen, Jialin
    Fan, Shuaikun
    Yan, Xiping
    Sun, Yanzhao
    PROCEEDINGS OF 2023 4TH INTERNATIONAL SYMPOSIUM ON ARTIFICIAL INTELLIGENCE FOR MEDICINE SCIENCE, ISAIMS 2023, 2023, : 177 - 183
  • [40] An Optimal Weight Method for CT Image Denoising
    Dinh Hoan Trinh
    Marie Luong
    Jean-Marie Rocchisani
    Canh Duong Pham
    Huy Dien Pham
    Francoise Dibos
    Journal of Electronic Science and Technology, 2012, (02) : 124 - 129