Processing of X-Ray Images

  • Sergei ChakhlovEmail author
Reference work entry


A general overview of X-ray image processing is presented. In this chapter, the simplest and most effective digital image processing algorithms and visualization techniques are briefly considered. The algorithms and techniques are illustrated by actual X-ray images. The most developing areas of X-ray image processing are outlined. References to X-ray image database, formats, and software are given.


  1. ASTM standard E 2339–08 (2008) Standard practice for digital imaging and communication in nondestructive evaluation (DICONDE). ASTM international, West Conshohocken, PA,, Accessed 3 Oct 2018
  2. Barghout L, Sheynin J (2013) Real-world scene perception and perceptual organization: lessons from computer vision. J Vision 13(9):709–709. Scholar
  3. Batenburg J, Sijbers J (2009) Adaptive thresholding of tomograms by projection distance minimization. Pattern Recogn 42(10):2297–2305. Scholar
  4. Bishop CM (2006) Pattern recognition and machine learning. Springer, New York 738pzbMATHGoogle Scholar
  5. Bukhari F, Dailey N (2013) Automatic radial distortion estimation from a single image. J Math Imag Vis 45(1):31–45. Scholar
  6. Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell PAMI 8(6):679–698. Scholar
  7. Carrasco M, Mery D (2004) Segmentation of welding defects using a robust algorithm. Mater Eval 62(11):1142–1147. Scholar
  8. Caselles V, Kimmel R, Sapiro G (1997) Geodesic active contours. Int J Comp Vision 22(1):61–79. Scholar
  9. Castleman K (1996) Digital image processing. Prentice-Hall, Englewood CliffsGoogle Scholar
  10. Chakhlov S, Lebedev M, Usachev E (2006) Method of X-ray image stitching. Testing Diagnostics, 2:34–40 (Russian)Google Scholar
  11. Chakhlov S, Kasyanov S, Kasyanov V, Osipov S, Stein M, Stein A, Sun X (2016) Betatron application in mobile and relocatable inspection systems for freight transport control. J Phys Conf Ser 671:012024 IOP Publishing. Scholar
  12. Gonzalez R, Woods R (2008) Digital image processing, 3rd edn. Prentice Hall, Upper Saddle RiverGoogle Scholar
  13. Grady L (2006) Random walks for image segmentation. IEEE Trans Pattern Anal Mach Intel 28(11):1768–1783. Scholar
  14. Grady L, Schwartz E (2006) Isoperimetric graph partitioning for image segmentation. IEEE Trans Pattern Anal Mach Intel 28(3):469–475. Scholar
  15. Hanes R (1949) The construction of subjective brightness scales from fractionation data: a validation. J Experiment Psych 39(5):719–728. Scholar
  16. ISee! (2017) BAM radiographic image analysis software. Accessed 3 Oct 2018
  17. Jobst M, Koetz A, Clendening S (2010) The value of diconde in multi-modal NDT environments. In: ECNDT 2010, pp 3912–3917Google Scholar
  18. Lindeberg T, Li X (1997) Segmentation and classification of edges using minimum description length approximation and complementary junction cues. Comp Vision Image Understand 67(1):88. Scholar
  19. Manduca A, Yu L, Trzasko J, Khaylova N, Kofler J, McCollough C, Fletcher J (2009) Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT. Med Phys 36(11):4911–4919. Scholar
  20. Mery D (2015) Computer vision for X-ray testing (imaging, systems, image databases and algorithms). Springer, Berlin 362p.,747CrossRefzbMATHGoogle Scholar
  21. Mery D (2018) BALU: a toolbox Mathlab for computer vision, pattern recognition and image processing. Accessed 3 Oct 2018
  22. Mery D, Riffo V, Zscherpel U, Mondragón G, Lillo I, Zuccar I, Lobel H, Carrasco M (2015) GDXray: the database of X-ray images for nondestructive testing. J Nondestruct Eval 34(4):42. Scholar
  23. Nock R, Nielsen F (2004) Statistical region merging. IEEE Trans Pattern Anal Mach Intel 26(11):1452–1458. Scholar
  24. Paris S, Kornprobst P, Tumblin J, Durand F (2009) Bilateral filtering: theory and applications. Found Trends Comput Graph Vis 4(1):1–73. Scholar
  25. Pratt W (2007) Digital image processing, 4th edn. Wiley, New YorkCrossRefGoogle Scholar
  26. Rakshit S, Ghosh A, Uma Shankar B (2007) Fast mean filtering technique (FMFT). Pattern Recogn 40:890–897. Scholar
  27. Rebuffel V, Dinten J-M (2007) Dual-energy X-ray imaging: benefits and limits. Insight Nondestruct Test Cond Monitor 49(10):589–594. Scholar
  28. Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Machine Intel 22(8):888–905. Scholar
  29. Tomasi C, Manduchi R (1998) Bilateral filtering for gray and color images. In: Proceedings of the 1998 I.E. international conference on computer vision, Bombay, India, pp. 839–846,
  30. Wu Z, Leahy R (1993) An optimal graph theoretic approach to data clustering: theory and its application to image segmentation. IEEE Trans Pattern Anal Mach Intel 15(11):1101–1113CrossRefGoogle Scholar
  31. Zahn C (1971) Graph-theoretical methods for detecting and describing gestalt clusters. IEEE Trans Comp 20(1):68–86CrossRefGoogle Scholar
  32. Zuiderveld K (1994) Contrast limited adaptive histogram equalization. In: Heckbert P (ed) Graphics gems IV. Academic Press, New York, pp 474–485.,156-1.50061-6CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Non-Destructive Testing and SecurityTomsk Polytechnic UniversityTomskRussia

Section editors and affiliations

  • Nathan Ida
    • 1
  • Norbert Meyendorf
    • 2
  1. 1.Department of Electrical and Computer EngineeringUniversity of AkronAkronUSA
  2. 2.Center for Nondestructive EvaluationIowa State UniversityAmesUSA

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