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Computer Aided Classification of Mammographic Tissue Using Shapelets and Support Vector Machines

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Artificial Intelligence: Methods and Applications (SETN 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8445))

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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, derive the source images that generate the observed ROS in mammograms. The Shapelet decomposition coefficients can be used efficiently to detect ROS areas using Support-Vector-Machines (SVMs) with radial basis function kernels. Extensive experiments using the Mammographic Image Analysis Society (MIAS) database have shown high recognition accuracy above 86% for all kinds of breast abnormalities that exceeds the performance of similar decomposition methods based on Zernike moments presented in the literature by more than 8%.

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References

  1. Feuer, E.J., Wun, L.M., Boring, C.C., Flanders, W.D., Timmel, M.J., Tong, T.: The lifetime risk of developing breast cancer. Journal of the National Cancer Institute 85(11), 892–897 (1993)

    Article  Google Scholar 

  2. Sirovich, B.E., Sox, H.C.: Breast cancer screening. Surgical Clinics of North America 79(5), 961–990 (1999)

    Article  Google Scholar 

  3. Martin, J.E., Moskowitz, M., Milbrath, J.R.: Breast cancer missed by mammography. American Journal of Roentgenology 132(5), 737–739 (1979)

    Article  Google Scholar 

  4. Kalisher, L.: Factors influencing false negative rates in xero-mammography. Radiology 133, 297 (1979)

    Google Scholar 

  5. Kerlikowske, K., Carney, P.A., Geller, B., Mandelson, M.T., Taplin, S.H., Malvin, K., Ballard-Barbash, R.: Performance of screening mammography among women with and without a first-degree relative with breast cancer. Annals of Internal Medicine 133(11), 855–863 (2000)

    Article  Google Scholar 

  6. Bird, R.E., Wallace, T.W., Yankaskas, B.C.: Analysis of cancers missed at screening mammography. Radiology 184(3), 613–617 (1992)

    Google Scholar 

  7. Tabar, L., Dean, B.P.: Teaching Atlas of Mammography, 2nd edn. Thieme, NY (1985)

    Google Scholar 

  8. Sampaio, W.B., Diniz, E.M., Silva, A.C., Cardoso de Paiva, A., Gattass, M.: Detection of masses in mammogram images using CNN, geostatistic functions and SVM. Computers in Biology and Medicine 41(8), 653–664 (2011)

    Article  Google Scholar 

  9. Pereira, D.C., Ramos, R.P., Zanchetta do Nascimento, M.: Segmentation and detection of breast cancer in mammograms combining wavelet analysis and genetic algorithm. Computer Methods and Programs in Biomedicine (2014)

    Google Scholar 

  10. Oliver, A., Freixenet, J., Martí, J., Pérez, E., Pont, J., Denton, E., Zwiggelaar, R.: A review of automatic mass detection and segmentation in mammographic images. Medical Image Analysis 14(2), 87–110 (2010)

    Article  Google Scholar 

  11. Wang, D., Shi, L., Heng, P.A.: Automatic detection of breast cancers in mammograms using structured support vector machines. Neurocomputing 72(13-15), 3296–3302 (2009)

    Article  Google Scholar 

  12. Eltoukhy, M.M., Faye, I., Samir, B.B.: A statistical based feature extraction method for breast cancer diagnosis in digital mammogram using multιresolution representation. Computers in Biology and Medicine 42(1), 123–128 (2012)

    Article  Google Scholar 

  13. Eltoukhy, M.M., Faye, I., Samir, B.B.: A comparison of wavelet and curvelet for breast cancer diagnosis in digital mammogram. Computers in Biology and Medicine 40(4), 384–391 (2010)

    Article  Google Scholar 

  14. Buciu, I., Gacsadi, A.: Directional features for automatic tumor classification of mammogram images. Biomedical Signal Processing and Control 6(4), 370–378 (2011)

    Article  Google Scholar 

  15. Ren, J.: ANN vs. SVM: Which one performs better in classification of MCCs in mammogram imaging. Knowledge-Based Systems 26, 144–153 (2012)

    Article  Google Scholar 

  16. Zanchetta do Nascimento, M., Martins, A.S., Neves, A.L., Ramos, R.P., Flores, E.L., Carrijo, G.A.: Classification of masses in mammographic image using wavelet domain features and polynomial classifier. Expert Systems with Applications 40(15), 6213–6221 (2013)

    Article  Google Scholar 

  17. Sampat, M.P., Markey, M.K., Bovik, A.C.: Computer-Aided Detection and Diagnosis in Mammography. In: Handbook of Image and Video Processing. Elsevier, London (2003)

    Google Scholar 

  18. Rangayyan, R.M., Ayres, F.J., Leo Desautels, J.E.: A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs. Journal of the Franklin Institute 344(3), 312–348 (2007)

    Article  MATH  Google Scholar 

  19. Refregier, A.: Shapelets–I. A method for image analysis. Monthly Notices of the Royal Astronomical Society 338(1), 35–47 (2003)

    Article  Google Scholar 

  20. Refregier, A., Bacon, D.: Shapelets—II. A method for weak lensing measurements. Monthly Notices of the Royal Astronomical Society 338(1), 48–56 (2003)

    Article  Google Scholar 

  21. Tahmasbi, A., Saki, F., Shokouhi, S.B.: Classification of benign and malignant masses based on Zernike moments. Computers in Biology and Medicine 41(8), 726–735 (2011)

    Article  Google Scholar 

  22. Scholkopf, B., Burges, C.J.C., Smola, A.J.: Advances in Kernel Methods. Support Vector Learning. The MIT Press, London (1999)

    Google Scholar 

  23. Zernike, F.: Diffraction theory of the cut and its improved form, the phase contrast method. Physica 1, 689–704 (1934)

    Article  MATH  Google Scholar 

  24. Teague, M.: Image analysis via the general theory of moments. J. Opt. Soc. Am. 70, 920–930 (1980)

    Article  MathSciNet  Google Scholar 

  25. Teh, C., Chin, R.: On image analysis by the methods of moments. IEEE Transactions on Pattern Analysis and Machine Intelligence 10, 496–513 (1988)

    Article  MATH  Google Scholar 

  26. Bailey, R., Srinath, M.: Orthogonal moment feature for use with parametric and non-parametric classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 18, 389–399 (1988)

    Article  Google Scholar 

  27. Khotanzad, A., Hong, Y.: Classification of invariant image representations using a neural network. IEEE Transactions on Acoustics and Speech Signal Processing 38, 1028–1038 (1990)

    Article  Google Scholar 

  28. http://peipa.essex.ac.uk/info/mias.html

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Author information

Authors and Affiliations

  1. Wired Communications Lab., Electrical and Computer Engineering Department, University of Patras, Greece

    George Apostolopoulos, Athanasios Koutras, Ioanna Christoyianni & Evaggelos Dermatas

  2. Informatics and Mass Media Department, Technical Educational Institute of Western Greece, Greece

    Athanasios Koutras

Authors
  1. George Apostolopoulos
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  2. Athanasios Koutras
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  3. Ioanna Christoyianni
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  4. Evaggelos Dermatas
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Ioannina, GR 45110, Ioannina, Greece

    Aristidis Likas

  2. Department of Computer Science, University of Ioannina, P.O. Box 1186, 45110, Ioannina, Greece

    Konstantinos Blekas

  3. Hellenic Open University, GR 26335, Peribola, Patras, Greece

    Dimitris Kalles

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Apostolopoulos, G., Koutras, A., Christoyianni, I., Dermatas, E. (2014). Computer Aided Classification of Mammographic Tissue Using Shapelets and Support Vector Machines. In: Likas, A., Blekas, K., Kalles, D. (eds) Artificial Intelligence: Methods and Applications. SETN 2014. Lecture Notes in Computer Science(), vol 8445. Springer, Cham. https://doi.org/10.1007/978-3-319-07064-3_44

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  • DOI: https://doi.org/10.1007/978-3-319-07064-3_44

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07063-6

  • Online ISBN: 978-3-319-07064-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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