Linear Array Industrial Computerized Tomography Quantitative Detection Method for Small Defects Based on Coefficients of Variation


A method for accurate and quantitative nondestructive testing of small defects inside metal components has been developed by experimentally investigating the grayscale distribution in computerized tomography (CT) images for a typical 304 stainless-steel material that is extensively used in industrial products. CT scans were performed as a function of the defect pore size. The grayscale level and the noise deviation distribution were measured in each CT image. The localized grayscale features where the defects are located in the CT image were studied using wavelet decomposition. A quantitative correlation between the defect size and the grayscale distribution in the CT images was established based on the coefficient of variation. The experimental findings, when combined with the advantages of an industrial CT detection method, allow for small defects to be characterized, imaged, and presented. The results show that the quantitative accuracy of this method is higher when compared with the traditional full-width at half-maximum method. The measurement process developed herein is simple and highly practicable and, furthermore, represents an effective way to characterize the size of small defects.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13


  1. 1.

    C.Z. Zhang, The technology and principle of industrial CT (Beijing: Science Press, 2009), p. 32.

    Google Scholar 

  2. 2.

    J. Hseih, Computed tomography—principles, design, artifacts and recent advances (Bellingham: SPIE Press, 2009), p. 1.

    Google Scholar 

  3. 3.

    P.J. Ni, J.T. Wang, M. Yan, and S.C. Zheng, J. Mech. Eng. 53, 13 (2017).

    Article  Google Scholar 

  4. 4.

    B.H. Jiang, S.Z. Yu, J.J. Dong, and K. Zhang, Comput. Sci. Eng. 02, 96 (2019).

    Google Scholar 

  5. 5.

    X.C. Zhang, X.L. Zhang, Q. Liu, J.T. Wang, and A. Liu, Nondestruct. Test. 41, 52 (2019).

    CAS  Google Scholar 

  6. 6.

    G.Y. Pan, M.X. Zhang, Y.L. Bao, and H.B. Zhang, Comput. Tomo. Theor. 28, 205 (2019).

    Google Scholar 

  7. 7.

    M. Askari, A. Taheri, M.M. Iarijani, and A. Movafeghi, Nucl. Instrum. Methods. 923, 109 (2019).

    CAS  Article  Google Scholar 

  8. 8.

    Y.L. Gao, R. Rui, L.J. Song, J. Zhang, F. Li, F. Zhang, and D.C. Hu, Nondestruct. Test. 36, 14 (2014).

    Google Scholar 

  9. 9.

    X.C. Zhang, L. Zhang, and J.T. Wang, Nondestruct. Test. 37, 20 (2015).

    Google Scholar 

  10. 10.

    Y. C. Wu, (2013)

  11. 11.

    W. Xu, (2012)

  12. 12.

    Z. Huang, F.Q. Gao, Z.Y. Zheng, C.J. Chen, and R. Li, Laser Part. Beams 26, 286 (2014).

    Google Scholar 

  13. 13.

    Z.C. Qi, P.J. Ni, J.Y. Shen, Z.M. Guo, and S.M. Tang, Ord. Mater. Sci. Eng. 40, 122 (2017).

    Article  Google Scholar 

  14. 14.

    M. Bartscher, M. Neukamm, M. Koch, Met. Ind. Int. Conf. 17–19 (2010)

  15. 15.

    M. Bartscher, U. Hilpert, and D. Fiedler, Tech. Mess. 75, 178 (2008).

    Article  Google Scholar 

  16. 16.

    K.J. Batenburg and J. Sijbers, Pattern Recognit. 42, 2297 (2009).

    Article  Google Scholar 

  17. 17.

    L. Zhu, CNKI:CDMD:2.1014.380474. (2014)

  18. 18.

    P.X. Chen, M.Q. Wang, S.H. Li, H.L. Hong, and Y. Wang, J. Graph. 36, 581 (2015).

    Google Scholar 

  19. 19.

    U. K. Bhowmik, D. Mandala, N. W. Hudyma, O. P. Kreidl, A. Harris, Proceedings of IEEE Southeast Conference at Griffin Gate Marriot Resort & Spa, 1–8 (2014)

  20. 20.

    S.L. Qi and M.Q. Zhang, J. Graph. 43, 17 (2013).

    Google Scholar 

  21. 21.

    C.M. Li, C. Y. Xu, C. F. Gui, M.D. Fox, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), 430–436 (2005)

  22. 22.

    Q.D. Li and L.D. Cai, Chin. J. Stereol. Image Anal. 16, 232 (2011).

    Google Scholar 

  23. 23.

    D. Wang and M. Zhu, Chin. J. Sci. Instrum. S2, 355 (2004).

    Google Scholar 

  24. 24.

    J.Y. Shi, R.P. Zhang, H.Y. Dong, and J.N. Gou, J. Lanzhou Jiaotong Univ. 36, 57 (2017).

    Google Scholar 

  25. 25.

    Z.C. Qi, P.J. Ni, W. Jiang, W.G. Zhang, and Z.M. Guo, High Power Laser Particle Beams 30, 124 (2018).

    Google Scholar 

  26. 26.

    P. Chen, J.X. Pan, and B. Liu, Opt. Precis. Eng. 17, 2269 (2009).

    Google Scholar 

  27. 27.

    W.G. Zhang, B. Han, P.J. Ni, Z.H. Zhang, Z.C. Qi, and K. Fu, Nondestruct. Test. 42, 50 (2020).

    Google Scholar 

  28. 28.

    GB/T 29067-2012, Accessed Access Date Access Year|.

Download references


This study was funded by the National Natural Science Foundation of China (61701446), Fund Project (JSZL2017208C001), Zhejiang Province Public Welfare Project (LGG20F010003), and Ningbo Science and Technology Service Demonstration Project (2019F1036).

Author information



Corresponding author

Correspondence to Zicheng Qi.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflicts of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Qi, Z., Ni, P., Jiang, W. et al. Linear Array Industrial Computerized Tomography Quantitative Detection Method for Small Defects Based on Coefficients of Variation. Journal of Elec Materi (2021).

Download citation


  • Industrial computerized tomography
  • wavelet decomposition
  • coefficient of variation
  • small defect quantification