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Image Enhancement with Applications in Biomedical Processing

  • Małgorzata CharytanowiczEmail author
  • Piotr Kulczycki
  • Szymon Łukasik
  • Piotr A. Kowalski
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 945)

Abstract

The images obtained by X-Ray or computed tomography (CT) may be contaminated with different kinds of noise or show lack of sharpness, too low or high intensity and poor contrast. Such image deficiencies can be induced by adverse physical conditions and by the transmission properties of imaging devices. A number of enhancement techniques in image processing may improve the quality of the image. These include: point arithmetic operations, smoothing and sharpening filters and histogram modifications. The choice of the technique, however, depends on the type of image deficiency. In this paper, the primary aim is to propose an efficient image enhancement method based on nonparametric estimation so as to enable medical images to have better contrast. To evaluate the method performance, X-Ray and CT images have been studied. Experimental results verify that applying this approach can engender good image enhancement performance when compared with classical techniques.

Keywords

Image enhancement Contrast stretching Nonparametric estimation Numerical algorithm X-ray images 

References

  1. 1.
    Charytanowicz, M., Kulczycki, P., Łukasik S., Kowalski, P.A.: Image enhancement with applications in biomedical processing. In: Kulczycki, P., Kowalski, P.A., Łukasik, S. (eds.) Contemporary Computational Science, p. 54. AGH-UST Press, Cracow (2018)Google Scholar
  2. 2.
    Charytanowicz, M., Kulczycki, P.: An image analysis algorithm for soil structure identification. In: Filev, D., Jabłkowski, J., Kacprzyk, J., Popchev, I., Rutkowski, L., Sgurev, V., Sotirova, E., Szynkarczyk, P., Zadrożny, S. (eds.) Information Technologies in Biomedicine, pp. 681–692. Springer, Cham (2014)Google Scholar
  3. 3.
    Charytanowicz, M., Niewczas, J., Kulczycki, P., Kowalski, P.A., Łukasik, S., Żak, S.: Complete gradient clustering algorithm for features analysis of X-ray images. In: Pietka, E., Kawa, J. (eds.) Information Technologies in Biomedicine, pp. 15–24. Springer, Heidelberg (2010)Google Scholar
  4. 4.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Prentice Hall, New Jersey (2007)Google Scholar
  5. 5.
    Kulczycki, P.: Estymatory jadrowe w analizie systemowej. WNT, Warszawa (2005)Google Scholar
  6. 6.
    Kulczycki, P.: Kernel estimators in industrial applications. In: Prasad, B. (ed.) Soft Computing Applications in Industry. Springer, Berlin (2008)Google Scholar
  7. 7.
    Kulczycki, P., Charytanowicz, M.: A complete gradient clustering algorithm formed with kernel estimators. Int. J. Appl. Math. Comput. Sci. 20, 123–134 (2010)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Kulczycki, P., Charytanowicz, M., Kowalski, P.A., Łukasik, S.: The complete gradient clustering algorithm: properties in practical applications. J. Appl. Stat. 39, 1211–1224 (2012)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Pereira, O., Torre, E., Garcés, E., Rodriguez, R.: Edge detection based on kernel density estimation. In: Proceedings of the 2017 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2017, pp. 1–24. CSREA Press (2017)Google Scholar
  10. 10.
    Silverman, B.W.: Density Estimation for Statistics and Data Analysis. Chapman and Hall, London (1986)CrossRefGoogle Scholar
  11. 11.
    Smolka, B., Budzan, S., Lukač, R.: Nonparametric design of impulsive noise removal in colour images. J. Med. Inform. Technol. 7, 3–14 (2004)Google Scholar
  12. 12.
    Sprawls, P.: Optimizing medical image contrast, detail and noise in the digital era. Med. Phys. Int. J. 2, 128–133 (2014)Google Scholar
  13. 13.
    Wand, M.P., Jones, M.C.: Kernel Smoothing. Chapman and Hall, London (1994)CrossRefGoogle Scholar
  14. 14.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600–612 (2004)CrossRefGoogle Scholar
  15. 15.
    Wojnar, L., Majorek, M.: Komputerowa analiza obrazu. Fotobit Design, Warszawa (1994)Google Scholar
  16. 16.
    Yang, Y.-Q., Zhang, J.-S., Huang, X.-F.: Adaptive image enhancement algorithm combining kernel regression and local homogeneity. Math. Probl. Eng. 2010, 1–14 (2010)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Małgorzata Charytanowicz
    • 1
    • 2
    Email author
  • Piotr Kulczycki
    • 1
    • 3
  • Szymon Łukasik
    • 1
    • 3
  • Piotr A. Kowalski
    • 1
    • 3
  1. 1.Centre of Information Technology for Data Analysis MethodsPolish Academy of Sciences, Systems Research InstituteWarsawPoland
  2. 2.Electrical Engineering and Computer Science FacultyLublin University of TechnologyLublinPoland
  3. 3.Faculty of Physics and Applied Computer Science, Division for Information Technology and BiometricsAGH University of Science and TechnologyCracowPoland

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