Adaptive Wiener filter based on Gaussian mixture model for denoising chest X-ray CT image

被引：0
|
作者：
Tabuchi, Motohiro [1 ]
Yamane, Nobumoto [1 ]
Morikawa, Yoshitaka [1 ]
机构：
[1] Okayama Univ, Grad Sch Nat Sci & Technol, Okayama 7008530, Japan
来源：
PROCEEDINGS OF SICE ANNUAL CONFERENCE, VOLS 1-8 | 2007年
关键词：
Gaussian mixture distribution model; expectation-maximization algorithm; Maximum a posteriori probability; phantom; adaptive Wiener filter;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Because the X-ray CT imaging has high spatial resolution, it becomes more important in diagnostic imaging. However the techniques of low dose imaging at X-ray mass examination or thin slice imaging provide degraded CT images by noise. The CT images have specific noise, called streak artifact. In this paper, we apply an adaptive Wiener filter (AWF) based on the Gaussian mixture distribution model (GMM), proposed previously to reduce Gaussian white noise. Simulation results show that a new AWF-GMM designed using high dose (original) CT image and low dose (observed) CT image pairs of chest phantom for training image set provides high restoration ability.
引用
收藏
页码：679 / 686
页数：8
相关论文
共 50 条
  • [21] Insulator Infrared Image Denoising Using Gaussian Mixture Model with Adaptive Component Selection
    Sun, Zhongwei
    Guo, Qingrui
    Ge, Xinyuan
    [J]. ICSP: 2008 9TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING, VOLS 1-5, PROCEEDINGS, 2008, : 1212 - 1215
  • [22] Image denoising in hybrid wavelet and quincunx diamond filter bank domain based on Gaussian scale mixture model
    Shanthi, S. Arnala
    Sulochana, C. Helen
    Latha, T.
    [J]. COMPUTERS & ELECTRICAL ENGINEERING, 2015, 46 : 384 - 393
  • [23] Learning-Based X-Ray Image Denoising Utilizing Model-Based Image Simulations
    Hariharan, Sai Gokul
    Kaethner, Christian
    Strobel, Norbert
    Kowarschik, Markus
    Albarqouni, Shadi
    Fahrig, Rebecca
    Navab, Nassir
    [J]. MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2019, PT VI, 2019, 11769 : 549 - 557
  • [24] Image denoising via patch-based adaptive Gaussian mixture prior method
    Nian Cai
    Yang Zhou
    Shengru Wang
    Bingo Wing-Kuen Ling
    Shaowei Weng
    [J]. Signal, Image and Video Processing, 2016, 10 : 993 - 999
  • [25] Image denoising via patch-based adaptive Gaussian mixture prior method
    Cai, Nian
    Zhou, Yang
    Wang, Shengru
    Ling, Bingo Wing-Kuen
    Weng, Shaowei
    [J]. SIGNAL IMAGE AND VIDEO PROCESSING, 2016, 10 (06) : 993 - 999
  • [26] Optimal noise removal using an adaptive Wiener filter based on a locally stationary Gaussian mixture distribution model for images
    Yamane, N
    Morikawa, Y
    Kawakami, Y
    Takahashi, H
    [J]. ELECTRONICS AND COMMUNICATIONS IN JAPAN PART III-FUNDAMENTAL ELECTRONIC SCIENCE, 2004, 87 (01): : 49 - 60
  • [27] Wavelet based Image Denoising using Weighted Highpass Filtering Coefficients and Adaptive Wiener Filter
    Saluja, Rubi
    Boyat, Ajay
    [J]. 2015 INTERNATIONAL CONFERENCE ON COMPUTER, COMMUNICATION AND CONTROL (IC4), 2015,
  • [28] Wavelet based Image Denoising using Weighted Highpass Filtering Coefficients and Adaptive Wiener Filter
    Saluja, Rubi
    Boyat, Ajay
    [J]. 2015 International Conference on Computing, Communication and Security (ICCCS), 2015,
  • [29] Multispectral Satellite Image Denoising via Adaptive Cuckoo Search-Based Wiener Filter
    Suresh, Shilpa
    Lal, Shyam
    Chen, Chen
    Celik, Turgay
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2018, 56 (08): : 4334 - 4345
  • [30] Regularization Parameter Selection for Gaussian Mixture Model Based Image Denoising Method
    Zhang, J. W.
    Liu, J.
    Zheng, Y. H.
    Wang, J.
    [J]. ADVANCES IN COMPUTER SCIENCE AND UBIQUITOUS COMPUTING, 2017, 421 : 291 - 297
12345