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Russian Journal of Nondestructive Testing

, Volume 53, Issue 2, pp 134–141 | Cite as

Estimating the efficiency of two algorithms for segmentation of digital radiation images of test objects

  • S. E. Vorobeichikov
  • V. A. Fokin
  • V. A. Udod
  • A. K. Temnik
Radiation Methods

Abstract

A mathematical model that describes digital radiation images of test objects is presented. Two algorithms are given for automatic segmentation of digital images distorted by additive noises. The efficiency of the algorithms is estimated based on mathematical modeling.

Keywords

test objects mathematical model digital radiation image segmentation algorithms 

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References

  1. 1.
    Vavilov, V.P., Infrakrasnaya termografiya i teplovoi kontrol'’ (Infrared Thermography and Thermal Testing), Moscow: Spektr, 2013.Google Scholar
  2. 2.
    Barkhatov, V.A, Recognizing imperfections with an artificial neural network of a special type, Russ. J. Nondestr. Testing, 2006, vol. 42, no. 2, pp. 92–100.CrossRefGoogle Scholar
  3. 3.
    Syryamkin, V.I. and Gorbachev, S.V, Applying neural network algorithms for the analysis of images of materials in X-ray microtomograms, Izv. Vyssh. Uchebn. Zaved., Fiz., 2013, vol. 56, no. 10/2, pp. 29–34.Google Scholar
  4. 4.
    Grigorchenko, S.A. and Kapustin, V.I, Recognizing imperfections with an artificial neural network of a special type, Russ. J. Nondestr. Testing, 2009, vol. 45, no. 9, pp. 648–659.CrossRefGoogle Scholar
  5. 5.
    Vorobeichikov, S.E., Fokin, V.A., Udod, V.A., and Temnik, A.K., A study of two image-recognition algorithms for the classification of flaws in a test object according to its digital image, Russ. J. Nondestr. Testing, 2015, vol. 51, no. 10, pp. 644–651.CrossRefGoogle Scholar
  6. 6.
    Han, Y.-P., Han, Y., Wang, L.-M., and Pan, J.-X., Development of X-ray digital radiography automatic inspection system for testing the interior structure of complex product, Binggong Xuebao/Acta Armamentarii, 2012, vol. 33, no. 7, pp. 881–885.Google Scholar
  7. 7.
    Tou, J.T. and Gonsalez, R.C, Pattern Recognition Principles, Reading, Massachusetts: Addison-Wesley Publishing Company, 1974.Google Scholar
  8. 8.
    Zhuravlev, Yu.I., Ob algebraicheskom podkhode k resheniyu zadach raspoznavaniya ili klassifikatsii. Problemy kibernetiki (On an Algebraic Approach to Solving Recognition or Classification Problems. Problems of Cybernetics), Moscow: Nauka, 1978.Google Scholar
  9. 9.
    Gurvich, A.K. and Vasil’ev, V.A, Estimating the configuration of flaws in metal production with plane-parallel surfaces by a truncated delta-method, Kontrol’. Diagn., 2013, no. 10, pp. 68–70.Google Scholar
  10. 10.
    Ogorodnikov, S.A., Identifying materials in linac-based radiation customs inspection, Cand. Sci. (Eng.) Dissertation, St. Petersburg, 2002.Google Scholar
  11. 11.
    Park, J.S. and Kim, J.K, Calculation of effective atomic number and normal density using a source weighting method in a dual energy x-ray inspection system, J. Korean Phys. Soc., 2011, vol. 59, no. 4, pp. 2709–2713.CrossRefGoogle Scholar
  12. 12.
    Gil, Y., Oh, Y., Cho, M., and Namkung, W, Radiography simulation on single-shot dual spectrum X-ray for cargo inspection system, Appl. Radiat. Isot., 2011, vol. 69, no. 2, pp. 389–393.CrossRefGoogle Scholar
  13. 13.
    Lorden, G, Procedures for reacting to a change in distribution, Annals. Math. Statist, 1971, no. 42, pp. 1897–1908.CrossRefGoogle Scholar
  14. 14.
    Pollak, M, Optimal detection of a change in distribution, Ann. Statist, 1985, no. 13, pp. 206–227.CrossRefGoogle Scholar
  15. 15.
    Vorobeichikov, S.E, On detection of changes in the average value in a sequence of random numbers, Avtom. Telemekh., 1998, no. 3, pp. 50–58.Google Scholar
  16. 16.
    Yanshin, V.V., Analiz i obrabotka izobrazhenii: printsipy i algoritmy (Analysis and Processing of Images: Principles and Algorithms), Moscow: Mashinostroenie, 1994.Google Scholar
  17. 17.
    Osipov, S.P., Libin, E.E., Chakhlov, S.V., Osipov, O.S., and Shtein, A.M, Parameter identification method for dual-energy X-ray imaging, NDT & E Int., 2015, vol. 76, pp. 38–42.CrossRefGoogle Scholar

Copyright information

© Pleiades Publishing, Ltd. 2017

Authors and Affiliations

  • S. E. Vorobeichikov
    • 1
  • V. A. Fokin
    • 2
  • V. A. Udod
    • 1
    • 3
  • A. K. Temnik
    • 3
  1. 1.Tomsk State UniversityTomskRussia
  2. 2.Siberian State Medical UniversityTomskRussia
  3. 3.Institute of Nondestructive TestingTomsk Polytechnic UniversityTomskRussia

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