Denoising X-ray CT Images based on Product Gaussian Mixture Distribution Models for Original and Noise Images

被引:2
|
作者
Tabuchi, Motohiro [1 ]
Yamane, Nobumoto [2 ]
机构
[1] Konko Hosp, Dept Radiol, Uramishinden 740 Asakuchi, Okayama 7190104, Japan
[2] Okayama Univ, Grad Sch Nat Sci & Technol, Okayama 7008530, Japan
来源
TENCON 2010: 2010 IEEE REGION 10 CONFERENCE | 2010年
关键词
D O I
10.1109/TENCON.2010.5686039
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
An adaptive Wiener filter for denoising X-ray CT image has been proposed based on the universal Gaussian mixture distribution model (UNI-GMM). In this method, the UNI-GMM is estimated by the statistical learning method using two sets of pari images, one of which is an observed (low dose) X-ray CT image set and the other is an original (high dose) X-ray CT image set. Owing to the physical limitations of CT scanners, the original (high dose) X-ray CT image also includes considerable noise that prevented precise learning of the UNI-GMM. On the other hand, the noise included in the X-ray CT images is the specific artifact which is called streak artifact and is known to be statistically non-stationary. In the previously proposed method, the artifact is treated to be stationary for simplicity. Thus the restored images include residual noise due to the non-stationary noise. In this paper, the UNI-GMM method is improved by a two stages product modeling. First, the UNI-GMM for the original images is estimated using a low noise natural image set that include scenes, portraits and still pictures, to prevent the effect of noise on the original (high dose) CT images. Second, the UNI-GMM for the noise images is estimated using a noise image set casted by subtracting the original X-ray CT images from the observed X-ray CT images. Simulation results show that the proposed product UNI-GMMs performs better than the conventional stationary noise model simply learned using X-ray CT images.
引用
收藏
页码:1679 / 1684
页数:6
相关论文
共 50 条
  • [41] A flexible patch based approach for combined denoising and contrast enhancement of digital X-ray images
    Irrera, Paolo
    Bloch, Isabelle
    Delplanque, Maurice
    MEDICAL IMAGE ANALYSIS, 2016, 28 : 33 - 45
  • [42] Denoising of Impulse Noise using Partition- Supported Median, Interpolation and DWT in Dental X-Ray Images
    Shajahan, Mohamed
    Aris, Siti Armiza Mohd
    Usman, Sahnius
    Noor, Norliza Mohd
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2022, 13 (09) : 274 - 280
  • [43] Deep Learning Models for Pneumonia Identification and Classification Based on X-Ray Images
    Naralasetti, Veeranjaneyulu
    Shaik, Reshmi Khadherbhi
    Katepalli, Gayatri
    Bodapati, Jyostna Devi
    TRAITEMENT DU SIGNAL, 2021, 38 (03) : 903 - 909
  • [44] QUALITY MEASURE OF THE COMPRESSED ECHO, X-RAY AND CT IMAGES
    Gupta, Rajani
    Bansod, Prashant
    Gamad, R. S.
    INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS, 2013, 13 (01)
  • [45] Quantification of spatial correlation in x-ray CT and MR images
    Lei, TH
    Udupa, JK
    2002 IEEE NUCLEAR SCIENCE SYMPOSIUM, CONFERENCE RECORD, VOLS 1-3, 2003, : 1097 - 1101
  • [46] Automated identification of intergranular corrosion in x-ray CT images
    Howell, PA
    Winfree, WP
    REVIEW OF PROGRESS IN QUANTITATIVE NONDESTRUCTIVE EVALUATION, VOLS 22A AND 22B, 2003, 20 : 568 - 574
  • [47] Estimators of tissue proportions from X-ray CT images
    Glasbey, CA
    Robinson, CD
    BIOMETRICS, 2002, 58 (04) : 928 - 936
  • [48] Morphometric analysis of X-ray and CT images for evaluating osteoporosis
    Shankar, N.
    Babu, S. Sathish
    Viswanathan, C.
    CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2019, 22 (Suppl 6): : 15111 - 15119
  • [49] Simulation of X-ray Projection Images for Dimensional CT Metrology
    Welkenhuyzen, F.
    Boeckmans, B.
    Kruth, J. -P.
    Dewulf, W.
    Voet, A.
    OPTICAL MEASUREMENT TECHNIQUES FOR STRUCTURES & SYSTEMS2 (OPTIMESS2012), 2013, : 477 - 487
  • [50] Morphometric analysis of X-ray and CT images for evaluating osteoporosis
    N. Shankar
    S. Sathish Babu
    C. Viswanathan
    Cluster Computing, 2019, 22 : 15111 - 15119