Application of Two-Dimensional Matched Filters to X-Ray Radiographic Flaw Detection and Enhancement

  • R. M. Wallingford
  • E. M. Siwek
  • J. N. Gray
Part of the Advances in Cryogenic Engineering book series (volume 28)


Detection and enhancement of low contrast flaws in radiographic images with high noise fields is an ongoing topic of research in nondestructive evaluation. In film radiography, the minimum detectable flaw thickness is controlled by the exposure characteristics and the flaw size in relation to the thickness of the part. The exposure characteristics determine the overall sensitivity and noise level, while the flaw thickness controls the contrast of the flaw image with respect to the background film density. Often it is difficult to generate optimal exposures when inspecting thick objects or complicated part geometries. This can result in noisy images due to the poor counting statistics of the photons as well as optical film densities that are suboptimal for visual interpretation. In addition, flaw contrast is often extremely low due to the flaw size or the poor orientation of crack-like flaws. The goal of the work presented in this paper is to demonstrate the utility of digital image processing using the matched filter for detecting and enhancing flaws in low-contrast, high-noise radiographic images. The basic theory of the matched filter will be presented along with its application to two-dimensional images, In addition several practical examples will be shown on simulated and real radiographs.


Optimal Filter Filter Element Flaw Shape Exposure Characteristic Spherical Flaw 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    D.O. North, “Analysis of the Factors which Determine Signal/Noise Discrimination in Radar”, RCA Laboratories, Princeton, N.J. Rept. PTR-6C, June, 1943.Google Scholar
  2. 2.
    S. Chaudhuri, S. Chatterjee, N. Katz, M. Nelson and M. Goldbaum, “Detection of Blood Vessels in Retinal Images Using Two Dimensional Matched Filters”, IEEE Transactions on Medical Imaging Vol. 8, No. 3, 263–269, 1989.CrossRefGoogle Scholar
  3. 3.
    W.A.C. Shmidt, “Modified Matched Filter for Cloud Clutter Suppression”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 12, No. 6, 594–600, 1990.CrossRefGoogle Scholar
  4. 4.
    L. O’Gorman and J.V. Nickerson, “Matched Filter Design for Fingerprint Enhancement”, IEEE International Conference on Acoustics, Speech and Signal Processing, 916–919, 1988.Google Scholar
  5. 5.
    Detection of Signals in Noise, A.D. Whalen, Academic Press, 1971.Google Scholar
  6. 6.
    Digital Image Processing, 2nd edition, W.K. Pratt, Wiley, 1991.MATHGoogle Scholar
  7. 7.
    Physics of Industrial Radiology, R. Halmshaw. American Elsevier, 1966.Google Scholar
  8. 8.
    J.N. Gray, “Three Dimensional Modeling of Projection Radiography” in Review of Progress in Quantitative Nondestructive Evaluation, Vol. 7A, D.O. Thompson and D.E. Chimenti, Ed., Plenum Press, 343–348, 1988.Google Scholar
  9. 9.
    J.N. Gray, F. Inanc and B.E. Shull, “ Three Dimensional Modeling of Projection Radiography” in Review of Progress in Quantitative Nondestructive Evaluation, Vol. 8A, D.O. Thompson and D.E. Chimenti, Ed., Plenum Press, 1989.Google Scholar

Copyright information

© Springer Science+Business Media New York 1992

Authors and Affiliations

  • R. M. Wallingford
    • 1
  • E. M. Siwek
    • 1
  • J. N. Gray
    • 1
  1. 1.Center for Nondestructive Evaluation and the FAA Center for Aviation Systems ReliabilityIowa State UniversityAmesUSA

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