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Computer Aided Diagnosis of Mammographic Tissue Using Shapelets in Quaternionic Representation

  • George ApostolopoulosEmail author
  • Athanasios Koutras
  • Ioanna Christoyianni
  • Evaggelos Dermatas
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 57)

Abstract

In this paper a robust regions-of-suspicion (ROS) diagnosis system on mammograms, recognizing all types of abnormalities is presented and evaluated. A new type of descriptors, based on Shapelet decomposition, estimate the source images that generate the observed ROS in mammograms. The Shapelet decomposition coefficients can be used to efficiently detect ROS areas using a new classifier base on quaternionic representation. Extensive experiments using the Mammographic Image Analysis Society (MIAS) database have shown high recognition accuracy over 86% for all kinds of breast, with less computational cost.

Keywords

Shapelets Quaternion Breast cancer Computer-Aided Diagnosis (CAD) Feature extraction 

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References

  1. 1.
    Ferlay, J., Shin, H. R., Bray, F., Forman, D., Mathers, C., & Parkin, D. M. (2010) Cancer Incidence and Mortality Worldwide: IARC CancerBase No. 10 [Internet]. International Agency for Research on Cancer.Google Scholar
  2. 2.
    Bray, F., Ren, J. S., Masuyer, E., & Ferlay, J. (2013) Global estimates of cancer prevalence for 27 sites in the adult population in 2008, International Journal of Cancer, 132(5), pp 1133-1145.Google Scholar
  3. 3.
    Ferlay, J., Soerjomataram, I., Dikshit, R., Eser, S., Mathers, C., Rebelo, D.M. Parkin, D. Forman & Bray, F. (2015) Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012, International Journal of Cancer, 136(5), E359-E386.Google Scholar
  4. 4.
    Sirovich, B. E., & Sox, H. C. (1999) Breast cancer screening, Surgical Clinics of North America, 79(5), pp 961-990.Google Scholar
  5. 5.
    Sampat, M. P., Markey, M. K., & Bovik, A. C. (2005) Computer-aided detection and diagnosis in mammography, Handbook of image and video processing, 2(1), pp 1195-1217.Google Scholar
  6. 6.
    Bird, R. E., Wallace, T. W., & Yankaskas, B. C. (1992) Analysis of cancers missed at screening mammography, Radiology, 184(3), pp 613-617.Google Scholar
  7. 7.
    Kerlikowske, K., Carney, P. A., Geller, B., Mandelson, M. T., Taplin, S. H., Malvin, K., Ernster V, Urban N, Cutter G, Rosenberg R, Ballard-Barbash R. (2000) Performance of screening mammography among women with and without a first-degree relative with breast cancer, Annals of Internal Medicine, 133(11), pp 855-863.Google Scholar
  8. 8.
    Miranda, G. H. B., & Felipe, J. C. (2014) Computer-aided diagnosis system based on fuzzy logic for breast cancer categorization, Computers in biology and medicine.Google Scholar
  9. 9.
    Dheeba, J., Singh, N. A., & Selvi, S. T. (2014) Computer-aided detection of breast cancer on mammograms: A swarm intelligence optimized wavelet neural network approach, Journal of biomedical informatics, 49, pp 45-52.Google Scholar
  10. 10.
    Wu, Z. Q., Jiang, J., & Peng, Y. H. (2008) Effective features based on normal linear structures for detecting microcalcifications in mammograms, In Pattern Recognition, 2008, ICPR 2008, 19th International Conference on (pp 1-4), IEEE.Google Scholar
  11. 11.
    Varela, C., Tahoces, P. G., Méndez, A. J., Souto, M., & Vidal, J. J. (2007) Computerized detection of breast masses in digitized mammograms, Computers in Biology and Medicine, 37(2), pp 214-226.Google Scholar
  12. 12.
    Meenalosini, S., & Janet, J. (2012) Computer aided diagnosis of malignancy in mammograms, European Journal of Scientific Research, 72(3), pp 360-368.Google Scholar
  13. 13.
    Catanzariti, E., Ciminello, M., & Prevete, R. (2003) Computer aided detection of clustered microcalcifications in digitized mammograms using Gabor functions, In Image Analysis and Processing, 2003, Proceedings, 12th International Conference on pp 266-270, IEEE.Google Scholar
  14. 14.
    Oliver, A., Torrent, A., Llado, X., & Marti, J. (2010) Automatic diagnosis of masses by using level set segmentation and shape description, In Pattern Recognition (ICPR), 2010 20th International Conference on pp 2528-2531, IEEE.Google Scholar
  15. 15.
    Liu, S., Babbs, C. F., & Delp, E. J. (2001) Multiresolution detection of spiculated lesions in digital mammograms, Image Processing, IEEE Transactions on, 10(6), pp 874-884.Google Scholar
  16. 16.
    Mousa, R., Munib, Q., & Moussa, A. (2005) Breast cancer diagnosis system based on wavelet analysis and fuzzy-neural, Expert systems with Applications, 28(4), pp 713-723.Google Scholar
  17. 17.
    Jasmine, J. L., Govardhan, A., & Baskaran, S. (2009) Microcalcification detection in digital mammograms based on wavelet analysis and neural networks, In Control, Automation, Communication and Energy Conservation, NCACEC, International Conference on pp 1-6, IEEE.Google Scholar
  18. 18.
    Mohamed, W., Alolfe, M., & Kadah, Y. M. (2008) Microcalcifications enhancement in digital mammograms using fractal modeling, In Biomedical Engineering Conference, CIBEC, Cairo International (pp 1-5), IEEE.Google Scholar
  19. 19.
    Vadivel, A., & Surendiran, B. (2013) A fuzzy rule-based approach for characterization of mammogram masses into BI-RADS shape categories, Computers in biology and medicine, 43(4), pp 259-267.Google Scholar
  20. 20.
    Moayedi, F., Azimifar, Z., Boostani, R., & Katebi, S. (2010) Contourlet-based mammography mass classification using the SVM family, Computers in biology and medicine, 40(4), pp 373-383.Google Scholar
  21. 21.
    Apostolopoulos, G., Koutras, A., Christoyianni, I., and Dermatas, E. (2014) Computer Aided Classification of Mammographic Tissue Using Shapelets and Support Vector Machines, In Artificial Intelligence: Methods and Applications, pp 510-520, Springer International Publishing.Google Scholar
  22. 22.
    Hamilton, W. R. (1844) On a new species of imaginary quantities connected with a theory of quaternions, In Proceedings of the Royal Irish Academy, pp 424-434.Google Scholar
  23. 23.
    Subakan, Ö. N., & Vemuri, B. C. (2011) A quaternion framework for color image smoothing and segmentation, International Journal of Computer Vision, 91(3), pp 233-250.Google Scholar
  24. 24.
    Ell, T., & Sangwine, S. J. (2007) Hypercomplex Fourier transforms of color images, Image Processing, IEEE Transactions on, 16(1), pp 22-35.Google Scholar
  25. 25.
    Alexiadis, D. S., & Sergiadis, G. D. (2009) Estimation of motions in color image sequences using hypercomplex Fourier transforms, Image Processing, IEEE Transactions on, 18(1), pp 168-187.Google Scholar
  26. 26.
    Sun, Y., Chen, S., & Yin, B. (2011) Color face recognition based on quaternion matrix representation, Pattern Recognition Letters, 32(4), pp 597-605.Google Scholar
  27. 27.
    Chen, B. J., Shu, H. Z., Zhang, H., Chen, G., Toumoulin, C., Dillenseger, J. L., & Luo, L. M. (2012) Quaternion Zernike moments and their invariants for color image analysis and object recognition, Signal Processing, 92(2), pp 308-318.Google Scholar
  28. 28.
    Kantor, I. L., & Solodovnikov, A. S. (1989) Hypercomplex numbers: an elementary introduction to algebras, Springer.Google Scholar
  29. 29.
    Hanson, A. J. (2005) Visualizing quaternions, In ACM SIGGRAPH Courses, ACM.Google Scholar
  30. 30.
    Refregier, A. (2003) Shapelets—I. A method for image analysis. Monthly Notices of the Royal Astronomical Society, 338(1), pp 35-47.Google Scholar
  31. 31.
    Huynh, D. Q. (2009) Metrics for 3D rotations: Comparison and analysis, Journal of Mathematical Imaging and Vision, 35(2), pp 155-164.Google Scholar
  32. 32.

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • George Apostolopoulos
    • 1
    Email author
  • Athanasios Koutras
    • 2
    • 1
  • Ioanna Christoyianni
    • 1
  • Evaggelos Dermatas
    • 1
  1. 1.Wired Communications Lab, Electrical and Computer Engineering DepartmentUniversity of PatrasPatrasGreece
  2. 2.Informatics and Mass Media DepartmentTechnical Educational Institute of Western GreecePatrasGreece

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