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Recognition of Breast Abnormalities Using Phase Features

  • MATHEMATICAL MODELS AND COMPUTATIONAL METHODS
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Abstract

Early detection of breast pathologies and proper treatment increase the likelihood of a cure, and, as a result, life expectancy. Currently, methods and algorithms for computer aided detection (CAD) systems are being actively developed. The traditional approach to designing such systems consists in selecting and calculating the features of the region of interest from the source data, followed by the selection of a model for their classification using machine learning methods. This paper proposes a method for detecting and classifying breast anomalies based on local energy and phase congruency and a controlled machine learning classifier. Experimental results are presented using a digital mammography dataset and evaluated using various performance criteria.

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REFERENCES

  1. F. Bray, J. Ferlay, I. Soerjomataram, R. L. Siegel, L. A. Torre, and A. Jemal, “Global cancer statistics 2018: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries,” CA: Cancer J. Clinic. 68, 394–424 (2018).

    Google Scholar 

  2. Methodological Recommendations for Using BI-RADS System in Mammographic Examination Giving, Ed. by A. Yu. Vasil’ev (Moscow Department of Healthcare, Moscow, 2017) [in Russian].

    Google Scholar 

  3. C. J. D’Orsi, E. A. Sickles, E. B. Mendelson, E. A. Morris, et al., ACR BI-RADS Atlas, Breast Imaging Reporting and Data System, Fifth ed. (American College of Radiology, Reston VA, 2013), pp. 1–79.

    Google Scholar 

  4. American College of Radiology. ACR Breast Imaging Reporting and Data System (BI-RADS) Website. https://www.acr.org.

  5. J. Bozek, M. Mustra, K. Delac, and M. Grgic, “A survey of image processing algorithms in digital mammography,” in Recent Advances in Multimedia Signal Processing and Communications (Springer, 2009), pp. 631–657.

    Google Scholar 

  6. A. D. Trister, D. S. M. Buist, and Ch. I. Lee, “Will machine learning tip the balance in breast cancer screening?” JAMA Oncology 3, 1463–1464 (2017).

  7. Luqman Mahmood Mina and Nor Ashidi Mat Isa, “A review of computer-aided detection and diagnosis of breast cancer in digital mammography,” J. Medical Sci. 15 (3), 110 (2015).

    Article  Google Scholar 

  8. M. S. Salama, A. S. Eltrass, and H. M. Elkamchouchi, “An improved approach for computer-aided diagnosis of breast cancer in digital mammography,” in Proc. IEEE Int. Symp. on Medical Measurements and Applications (MeMeA), Rome, June, 11–13, 2018 (IEEE, New York, 2018), pp. 1–5.

  9. K. Doi, “Computer-aided diagnosis in medical imaging: historical review, current status and future potential,” Comput. Med. Imaging Graph. 31 (4), 198–211 (2017).

  10. D. Halalli and A. Makandar, Computer Aided Diagnosis – Medical Image Analysis Techniques, Breast Imaging, by ed. C. M. Kuzmiak, (IntechOpen, Jan. 17th, 2018), https://doi.org/10.5772/intechopen.69792.

  11. V. Kober, “Robust and efficient algorithm of image enhancement,” IEEE Trans. Consumer Electron. 52, 655–659 (2006).

  12. V. Kober, M. Mozerov, and J. Alvarez-Borrego, “Nonlinear filters with spatially-connected neigh-borhoods,” Opt. Eng. 40, 971–983 (2001).

  13. V. Kober, M. Mozerov, J. Alvarez-Borrego, and I. A. Ovseyevich, “Fast algorithms of rank-order filters with spatially adaptive neighborhoods,” Pattern Recognit. & Image Anal. 11, 690–698 (2001).

  14. F. Attneave, “Some informational aspects of visual perception,” Psychol. Rev. 61 (9), 183 (1954).

    Article  Google Scholar 

  15. M. C. Morrone and D. C. Burr, “Feature detection in human vision: A phase-dependent energy model,” Proc. R. Soc. London B: Biolog. Sci. 235 (1280), 221–245 (1988).

  16. B. Robbins and R. Owens, “2d feature detection via local energy,” Image & Vision Comput. 15, 353–368 (1997).

  17. P. Kovesi, “Image features from phase congruency,” Videre: J. Comput. Vision Res. 1, 1–26 (1999).

  18. M. Felsberg and G. Sommer, “The monogenic signal,” IEEE Trans. Signal Process. 49, 3136–3144 (2001).

  19. M. Felsberg and G. Sommer, “The monogenic scale-space: A unifying approach to phase-based image processing in scale-space,” J. Math. Imaging & Vision 21, 5–26 (2004).

  20. J. Diaz-Escobar, V. Kober, V. Karnaukhov, and J. Gonzalez-Fraga, “A new invariant to illumination feature descriptor for pattern recognition,” J. Commun. Technol. Electron. 63, 1469–1474 (2018).

  21. J. Diaz-Escobar, V. Kober, and J. Gonzalez-Fraga, “Luift: Lumination invariant feature transform,” Math. Probl. Eng. 2018, ID 3758102 (2018).

  22. P. Kovesi et al., “Edges are not just steps,” in Proc. Fifth Asian Conf. on Computer Vision, Melbourne, 2002, Vol. 8, pp. 22–28.

  23. J. Diaz-Escobar and V. Kober, “Text detection in natural scenes with phase congruency approach,” SPIE Opt. Eng. Appl. 10396, 1039637 (2017).

    Google Scholar 

  24. Yao Li, Wenjing Jia, Chunhua Shen, and Anton van den Hengel, “Characterness: An indicator of text in the wild,” IEEE Trans. Image Process. 23, 1666–1677 (2014).

  25. J. Suckling et al. “The mammographic image analysis society digital mammogram database,” Mammographic Image Analysis Society (MIAS) database v1.21 [Dataset], (2015). https://www.repository.cam.ac.uk/handle/1810/ 250394.

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Authors and Affiliations

  1. Ensenada Center for Scientific Research and Higher Education, 22860, Ensenada, Mexico

    J. Diaz-Escobar & V. Kober

  2. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, 127051, Moscow, Russia

    V. Kober, V. Karnaukhov & M. Mozerov

  3. Chelyabinsk State University, 454001, Chelyabinsk, Russia

    V. Kober

Authors
  1. J. Diaz-Escobar
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  2. V. Kober
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  3. V. Karnaukhov
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  4. M. Mozerov
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Corresponding authors

Correspondence to V. Kober, V. Karnaukhov or M. Mozerov.

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Translated by E. Oborin

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Diaz-Escobar, J., Kober, V., Karnaukhov, V. et al. Recognition of Breast Abnormalities Using Phase Features. J. Commun. Technol. Electron. 65, 1476–1483 (2020). https://doi.org/10.1134/S1064226920120050

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  • DOI: https://doi.org/10.1134/S1064226920120050

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