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Microcalcification Detection in Mammograms Based on Fuzzy Logic and Cellular Automata

  • Yoshio Rubio
  • Oscar Montiel
  • Roberto Sepúlveda
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 667)

Abstract

In the early diagnosis of breast cancer, computer-aided diagnosis (CAD) systems help in the detection of abnormal tissue. Microcalcifications can be an early indication of breast cancer. This work describes the implementation of a new method for the detection of microcalcifications in mammographies. The images were obtained from the mini-MIAS database. In the proposed method, the images are preprocessed using an x and y gradient operators, the output of each filter is the input of a fuzzy system that will detect areas with high-tone variation. The next step consists of a cellular automaton that uses a set of local rules to eliminate noise and keep the pixels with higher probabilities of belonging to a microcalcification region. Comparative results are presented.

Keywords

Breast cancer Microcalcification Mammography image Image enhancement Fuzzy system Cellular automata 

Notes

Acknowledgments

We thank Instituto Politécnico Nacional (IPN), the Commission of Operation and Promotion of Academic Activities of IPN (COFAA), and the Mexican National Council of Science and Technology (CONACYT) for supporting our research activities.

References

  1. 1.
    International Agency for Research on Cancer (2012). GLOBOCAN 2012: Estimated Cancer Mortality and Prevalence Worldwide in 2012. http://globocan.iarc.fr/Pages/fact_sheets_cancer.aspx. Accessed 1 Dec 2015
  2. 2.
    American Cancer Society Inc. Cancer facts and figures 2015. http://www.cancer.org/acs/groups/content/@editorial/documents/document/acspc-044552.pdf. Accessed 6 Sep 2015.
  3. 3.
    World Health Organization (2015) Cancer Country Profiles (2014). http://www.who.int/cancer/country-profiles/mex_en.pdf?ua=1. Accessed 1 Sep 2015
  4. 4.
    Paredes ES (2007) Atlas of mammography. Lippincott Williams and WilkinsGoogle Scholar
  5. 5.
    Benmazou S, Merouani HF, Layachi S, Nedjmeddine B (2014). Classification of mammography images based on cellular automata and Haralick parameters. In: Evolving Systems 5(3):209-216Google Scholar
  6. 6.
    Oliver A, Freixenet J, Martí J, Pérez E, Pont J, Denton E, Zwiggelaar R (2009) A review of automatic mass detection and segmentation in mammographic images. Medical Image Analysis, vol. 14. Elsevier, pp. 87-110Google Scholar
  7. 7.
    Nawalade Y (2009) Evaluation of Breast Calcification. The Indian Journal of Radiology and Imaging, 19:282-286Google Scholar
  8. 8.
    Balakumaran T, Vennila I, Shankar CG (2010) Detection of Microcalcification in Mammograms Using Wavelet Transform and Fuzzy Shell Clustering. International Journal of Computer Science and Information Security 7(1):121-125Google Scholar
  9. 9.
    Mohanalin J, Kalra PK, Kumar N (2008) Fuzzy based micro calcification segmentation. In: International Conference on Electrical and Computer Engineering, 2008. ICECE 2008. Dec 2008. pp 49-52Google Scholar
  10. 10.
    American Cancer Society Inc. (2015) Cancer Facts and Figures (2015). http://www.cancer.org/acs/groups/content/@editorial/documents/document/acspc-044552.pdf. Accessed 6 Sep 2015
  11. 11.
    Pereira DC, Ramos RP, do Nascimento MZ (2014). Segmentation and detection of breast cancer in mammograms combining wavelet analysis and genetic algorithm. Computer Methods and Programs in Biomedicine, 114:88-101Google Scholar
  12. 12.
    Berber T, Alpkocak A, Balci P, Dicle O (2012) Breast mass contour segmentation algorithm in digital mammograms. Computer Methods and Programs in Biomedicine 110:150-159Google Scholar
  13. 13.
    Viher B, Dobnikar A, Zazula D (1998) Cellular automata and follicle recognition problem and possibilities of using cellular automata for image recognition purposes. International Journal of Medical Informatics 49:231-241Google Scholar
  14. 14.
    Qadir F, Peer MA, Khan KA (2012) Efficient edge detection methods for diagnosis of lung cancer based on two-dimensional cellular automata. Advances in Applied Science Research 3(4):2050-2058Google Scholar
  15. 15.
    Wongthanavasu S, Tangvoraphonkcha V (2007) Cellular Automata-Based Algorithm and its Application in Medical Image Processing. In: ICIP 2007. IEEE International Conference on Image Processing 50:11-13Google Scholar
  16. 16.
    Anitha J, Peter JD (2015) Mammogram segmentation using maximal cell strength updation in cellular automata. Medical & Biological Engineering & Computing vol. 53(8):737-749Google Scholar
  17. 17.
    Cordeiro FR, Santos WP, and Silva-Filho AG (2014) Segmentation of mammography by applying growcut for mass detection. In: Liu J, Doi K, Fenster A (ed) MEDINFO 2013: Studies in Health Technology and Informatics, vol. 192. IOS Press, pp. 87-91Google Scholar
  18. 18.
    Kumar T, Sahoo G (2010) A novel method of edge detection using cellular automata. International Journal of Computer Applications 9:38-44Google Scholar
  19. 19.
    Hadi R, Saeed S, Hamid A (2013) A modern approach to the diagnosis of breast cancer in women based on using Cellular Automata In: First Iranian Conference on Pattern Recognition and Image Analysis (PRIA) 2013, pp 1-5Google Scholar
  20. 20.
    Cheng H, Lui YM, Freimanis RI (1996) A new approach to microcalcification detection in digital mammograms. In: Nuclear Science Symposium, Nov 1996. Conference Record, vol. 2. IEEE, pp 1094-1098Google Scholar
  21. 21.
    Cheng H, Lui YM, Freimanis RI (1998) A novel approach to microcalcification detection using fuzzy logic technique. IEEE Transactions on Medical Imaging. 17(3):442-450Google Scholar
  22. 22.
    Cheng HD, Wang J, Shi X (2004) Microcalcification detection using fuzzy logic and scale space approaches. Pattern Recognition, vol 37. Elsevier, pp 363-375Google Scholar
  23. 23.
    Cheng HD, Wang J (2003) Fuzzy logic and scale space approach to microcalcification detection. In: 2003 IEEE International Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP ‘03). April 2003, 2:345-348Google Scholar
  24. 24.
    Chen X, Chen Y (2010) An Improved Edge Detection in Noisy Image Using Fuzzy Enhancement. In: International Conference on Biomedical Engineering and Computer Science (ICBECS), Apr 2010. pp 1-4Google Scholar
  25. 25.
    Begum S, Devi O (2011) Fuzzy Algorithms for Pattern Recognition in Medical Diagnosis. Physical Sciences and Technology 7(2):1-12Google Scholar
  26. 26.
    Pandey N, Salcic Z, Sivaswamy J (2000) Fuzzy logic based microcalcification detection. In: Proceedings of the 2000 IEEE Signal Processing Society Neural Networks for Signal Processing X. 2:662-671Google Scholar
  27. 27.
    Chumklin S, Auephanwiriyakul S, Theera-Umpon, N (2010) Microcalcification detection in mammograms using interval type-2 fuzzy logic system with automatic membership function generation. In: IEEE International Conference on Fuzzy Systems, July 2010. pp 1-7Google Scholar
  28. 28.
    Bhattacharya M, Das A (2007) Fuzzy Logic Based Segmentation of Microcalcification in Breast Using Digital Mammograms Considering Multiresolution. In: International Machine Vision and Image Processing Conference, 2007. IMVIP 2007. pp 98-105Google Scholar
  29. 29.
    Quintanilla-Dominguez J, Ojeda-Magaña B, Cortina-Januchs MG, Ruelas R, Vega-Corona A, Andina D (2011) Image segmentation by fuzzy and possibilistic clustering algorithms for the identification of microcalcifications. Scientia Iranica Transactions: Computer Science & Engineering and Electrical Engineering 18:580–589Google Scholar
  30. 30.
    Kulkarni A (2001) Computer vision and fuzzy-neural systems. Prentice HallGoogle Scholar
  31. 31.
    Jantzen J (2013) Foundations of Fuzzy Control: A practical Approach. John Wiley & Sons, Second Edition.Google Scholar
  32. 32.
    Jang J, Sun C, Mizutani E (1997) Neuro-fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice HallGoogle Scholar
  33. 33.
    Espinosa J, Vandewalle J, Wetz V (2004) Fuzzy Logic, Identification and Predictive Control. Springer-VerlagGoogle Scholar
  34. 34.
    Von Newmann J, Burks A (1966) Theory of Self-Reproducing Automata. University of Illinois Press, 1966.Google Scholar
  35. 35.
    Wolfram S (1983) Reviews of Modern Physics, Statistical Mechanics of Cellular Automata. The American Physical Society 55: 601-643Google Scholar
  36. 36.
    Kari J (2005) Theory of cellular automata: A survey. Theoretical Computer Science 334:3-333Google Scholar
  37. 37.
    Suckling J (1994) The mini-MIAS database of mammograms. http://peipa.essex.ac.uk/info/mias.html. Acceded 5 Nov 2015

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yoshio Rubio
    • 1
  • Oscar Montiel
    • 1
  • Roberto Sepúlveda
    • 1
  1. 1.Instituto Politécnico NacionalCentro de Investigación y Desarrollo de Tecnología Digital (CITEDI-IPN)TijuanaMexico

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