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Pattern Recognition and Image Analysis

, Volume 28, Issue 4, pp 637–645 | Cite as

Methods of Intellectual Analysis in Medical Diagnostic Tasks Using Smart Feature Selection

  • N. Yu. Ilyasova
  • A. S. Shirokanev
  • A. V. Kupriyanov
  • R. A. Paringev
  • D. V. Kirsh
  • A. V. Soifer
Proceedings of the 6th International Workshop
  • 1 Downloads

Abstract

The paper deals with a computer technique for high-performance processing, analysis and interpretation of medical and diagnostic images. We propose a new approach to the analysis of different classes of images based on evaluation of aggregate geometric and texture parameters of allocated regions of interest which are supposed to be a basic feature set. The developed efficient feature-space generation technique is based on Big Data mining of unstructured information by applying the discriminative analysis methods. The technique makes it possible to extract regions of interest on fundus images containing four classes of objects: exudates, intact areas, thick vessels, and thin vessels. The use of Big Data technology made it possible, due to involving large amounts of data, to improve the training sample and reduce classification errors that ensured an increase of diagnosis accuracy up to 95%. The proposed technique has been applied to the coagulate location problem, that is a crucial problem of diabetic retinopathy treatment. The experiment results on real eye fundus images proved a considerable increase of treatment effectiveness.

Keywords

image processing laser coagulation sphere close packing coagulate location Big Data medical diagnostics 

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References

  1. 1.
    C. K. Emani, N. Cullot, and C. Nicolle, “Understandable Big Data: A survey,” Comput. Sci. Rev. 17, 70–81 (2015).MathSciNetCrossRefGoogle Scholar
  2. 2.
    A. Gandomi and M. Haider, “Beyond the hype: Big data concepts, methods, and analytics,” Int. J. Inf. Manage. 35 (2), 137–144 (2015).CrossRefGoogle Scholar
  3. 3.
    H. Özköse, E. S. Ari, and C. Gencer, “Yesterday, today and tomorrow of Big Data,” Procedia–Soc. Behav. Sci. 195, 1042–1050 (2015).CrossRefGoogle Scholar
  4. 4.
    E. Kolker, E. Stewart, and V. Özdemir, “Opportunities and challenges for the life sciences community,” OMICS 16 (3), 138–147 (2012).CrossRefGoogle Scholar
  5. 5.
    N. Ilyasova, “Computer systems for geometrical analysis of blood vessels diagnostic images,” Opt. Mem. Neural Networks (Inf. Opt.) 23 (4), 278–286 (2014).CrossRefGoogle Scholar
  6. 6.
    N. Yu. Ilyasova, “Methods for digital analysis of human vascular system. Literature review,” Comput. Opt. 37 (4), 517–541 (2013) [in Russian].CrossRefGoogle Scholar
  7. 7.
    V. M. Simchera, Methods of Multivariate Statistical Data Analysis (Finansy i Statistika, Moscow, 2008) [in Russian].Google Scholar
  8. 8.
    M. R. K. Mookiah, U. R. Acharya, C. M. Lim, A. Petznick, and J. S. Suri, “Data mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy features,” Knowl.–Based Syst. 33, 73–82 (2012).CrossRefGoogle Scholar
  9. 9.
    N. Yu. Ilyasova, A. V. Kupriyanov, and R. A. Paringer, “Formation features for improving the quality of medical diagnosis based on the discriminant analysis methods,” Comput. Opt. 38 (4), 851–855 (2014) [in Russian].CrossRefGoogle Scholar
  10. 10.
    N. Yu. Ilyasova, R. A. Paringer, A. V. Kupriyanov, and N. S. Ushakova, “The effective features formation for the identification of regions of interest in a fundus images,” in Proc. Int. Conf. Information Technology and Nanotechnology (ITNT 2016), CEUR Workshop Proceedings, vol. 1638, pp. 788–795, CEUR–WS.org (2016).Google Scholar
  11. 11.
    N. Yu. Ilyasova, A. V. Kupriyanov, and R. A. Paringer, “The discriminative analysis application to refine the diagnostic features of blood vessels images,” Opt. Mem. Neural Networks (Inf. Opt.) 24 (4), 309–313 (2015).CrossRefGoogle Scholar
  12. 12.
    E. Biryukova, R. Paringer, and A. V. Kupriyanov, “Development of the effective set of features construction technology for texture image classes discrimination,” in Proc. Int. Conf. Information Technology and Nanotechnology (ITNT 2016), CEUR Workshop Proceedings, vol. 1638, pp. 263–269, CEUR–WS.org (2016).Google Scholar
  13. 13.
    N. Yu. Ilyasova and A. V. Kupriyanov, “The Big Data mining to improve medical diagnostics quality,” in Proc. Int. Conf. Information Technology and Nanotechnology (ITNT 2015), CEUR Workshop Proceedings, vol. 1490, pp. 346–354, CEUR–WS.org (2015).Google Scholar
  14. 14.
    N. Yu. Ilyasova, A. V. Kupriyanov, and R. A. Paringer, “Formation of features for improving the quality of medical diagnosis based on discriminant analysis method,” Comput. Opt. 38 (4), 851–856 (2014). (In Russian).CrossRefGoogle Scholar
  15. 15.
    N. Ilyasova, R. Paringer, and A. Kupriyanov, “Regions of interest in a fundus image selection technique using the discriminative analysis methods,” in Computer Vision and Graphics, Proc. Int. Conf. ICCVG 2016, Ed. by L. J. Chmielewski et al., Lecture Notes in Computer Science (Springer, Cham, 2016), Vol. 9972, pp. 408–417.Google Scholar
  16. 16.
    M. Strzelecki, P. Szczypinski, A. Materka, and A. Klepaczko, “A software tool for automatic classification and segmentation of 2D/3D medical images,” Nucl. Instrum. Methods Phys. Res., Sect. A 702, 137–140 (2013).CrossRefGoogle Scholar
  17. 17.
    P. M. Szczypiński, M. Strzelecki, A. Materka, and A. Klepaczko, “MaZda–A software package for image texture analysis,” Comput. Methods Programs Biomed. 94 (1), 66–76 (2009).CrossRefGoogle Scholar
  18. 18.
    K. Fukunaga, Introduction to Statistical Pattern Recognition (Academic Press, New York and London, 1972).zbMATHGoogle Scholar
  19. 19.
    N. Ilyasova, R. Paringer, A. Kupriyanov, and N. Ushakova, “The effective features formation for the identification of regions of interest in a fundus images,” CEUR Workshop Proceedings, 2016, vol. 1638, pp. 788–795.Google Scholar
  20. 20.
    N. Ilyasova, R. Paringer, and A. Kupriyanov, “Regions of interest in a fundus image selection technique using the discriminative analysis methods,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, vol. 9972, pp. 408–417.Google Scholar
  21. 21.
    A. L. Kazakov, and P. D. Lebedev, “Algorithms of optimal packing construction for planar compact sets,” Vychisl. Metody Programm. 16 (2), 307–317 (2015) [in Russian].Google Scholar
  22. 22.
    A. L. Kazakov, A. A. Lempert, and H. L. Nguyen, “An algorithm of packing congruent circles in a multiply connected set with non–Euclidean metrics,” Vychisl. Metody Programm. 17 (2), 177–188 (2016).Google Scholar
  23. 23.
    G. N. Yas’kov, “Method of decision of task of packing of different circles with choice of perspective initial points,” Scientific Works of Kharkiv National Air Force University, No. 3 (25), 119–122 (2010).Google Scholar
  24. 24.
    S. I. Galiev and M. S. Lisafina, “Linear models for the approximate solution of the problem of packing equal circles into a given domain,” Eur. J. Oper. Res. 230 (3), 505–514 (2013).MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    E. A. Zamyckij, “Laser treatment of diabetic macular edema,” Aspirantskiy Vestnik Povolzhiya, No. 1–2, 74–80 (2015) [in Russian].Google Scholar

Copyright information

© Pleiades Publishing, Ltd. 2018

Authors and Affiliations

  • N. Yu. Ilyasova
    • 1
    • 2
  • A. S. Shirokanev
    • 1
    • 2
  • A. V. Kupriyanov
    • 1
    • 2
  • R. A. Paringev
    • 1
    • 2
  • D. V. Kirsh
    • 1
    • 2
  • A. V. Soifer
    • 1
    • 2
  1. 1.IPSI RAS – Branch of the FSRC “Crystallography and Photonics” RASSamaraRussia
  2. 2.Samara National Research UniversitySamaraRussia

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