Skip to main content
Log in

Influence of Primary Processing on the Segmentation of X-ray Images of Welds

  • Published:
Materials Science Aims and scope

We study the influence of preliminary processing of digital X-ray images by their median filtering and brightness normalization by nonsharp masking and contrast enhancement on the basis of a logarithmictype model on the results of testing. Examples of experimental detection of defects in welds of various types are presented.

This is a preview of subscription content, log in via an institution to check access.

Access this article

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. J. Shao, D. Du, H. Shi, et al., “Automatic weld recognition and extraction from real-time X-ray images using quadratic curve fitting and multi-order differences analysis of intensity profile,” Insight, 53, No. 10, 562–569 (2011).

    Article  Google Scholar 

  2. J. Hassan, A. Majid Awan, and A. Jalil, “Welding defect detection and classification using geometric features,” in: Proc. of the IEEE 10th Internat. Conf. on the Frontiers of Information Technology (December 17–19, 2012, Islamabad), Islamabad (2012), pp. 139–144.

  3. M. Kroetz, T. M. Centeno, M. R. Delgado, et al., “Genetic algorithms to automatic weld bead detection in double wall double image digital radiographs,” in: J. Liu and C. Alippi (editors), Proc. of the WCCI 2012 IEEE World Congr. on Computational Intelligence (June 10–15, 2012, Brisbane, Australia), Springer (2012), pp. 1–7.

  4. T. Mandziy, “Active shape models with adaptive weights,” Vidbir Obrob. Inform., Issue 30 (106), 133–137 (2009)

  5. T. F. Cootes, C. J. Taylor, D. H. Cooper, and J. Graham, “Active shape models – their training and application,” Comput. Vision Image Understand., 61(1), 38–59 (1995).

    Article  Google Scholar 

  6. V. R. Rathod and R. S. Anand, “A novel method for detection and quantification of incomplete penetration type flaws in weldments,” J. X-Ray Sci. Technol., 19, 261–274 (2011).

    Google Scholar 

  7. R. A. Vorobel’, “Logarithmic processing of images. Part 1: Basic model,” Vidbir Obrob. Inform., Issue 31 (107), 26–35 (2009)

  8. R. A. Vorobel’, Logarithmic Processing of Images [in Ukrainian], Naukova Dumka, Kyiv (2012).

  9. V. V. Botsyan and R. A. Vorobel’, “Improvement of images with the help of an adaptive logarithmic-type model,” Vidbir Obrob. Inform., Issue 37(113), 86–92 (2012).

    Google Scholar 

  10. I. B. Ivasenko, “Localization and segmentation of objects on different scales with the use of information function,” Vidbir Obrob. Inform., Issue 22(98), 76–80 (2005).

  11. R. A. Vorobel’, N. V. Opyr, Z. А. Bernyk, and O. R. Beregulyak, “Computer technology for the determination of sensitivity of X-ray testing according to an image of a grooved standard,” Defektoskopiya, No. 5, 81–89 (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to R. A. Vorobel.

Additional information

Translated from Fizyko-Khimichna Mekhanika Materialiv, Vol. 49, No. 4, pp. 48–55, July–August, 2013.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vorobel, R.A., Ivasenko, I.B., Mandzii, T.S. et al. Influence of Primary Processing on the Segmentation of X-ray Images of Welds. Mater Sci 49, 469–477 (2014). https://doi.org/10.1007/s11003-014-9638-2

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11003-014-9638-2

Keywords

Navigation