Comparison of Multi-resolution Analysis Patterns for Texture Classification of Breast Tumors Based on DCE-MRI

  • Alexia TzalavraEmail author
  • Kalliopi Dalakleidi
  • Evangelia I. Zacharaki
  • Nikolaos Tsiaparas
  • Fotios Constantinidis
  • Nikos Paragios
  • Konstantina S. Nikita
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10019)


Although Fourier and Wavelet Transform have been widely used for texture classification methods in medical images, the discrimination performance of FDCT has not been investigated so far in respect to breast cancer detection. Ιn this paper, three multi-resolution transforms, namely the Discrete Wavelet Transform (DWT), the Stationary Wavelet Transform (SWT) and the Fast Discrete Curvelet Transform (FDCT) were comparatively assessed with respect to their ability to discriminate between malignant and benign breast tumors in Dynamic Contrast-Enhanced Magnetic Resonance Images (DCE-MRI). The mean and entropy of the detail sub-images for each decomposition scheme were used as texture features, which were subsequently fed as input into several classifiers. FDCT features fed to a Linear Discriminant Analysis (LDA) classifier produced the highest overall classification performance (93.18 % Accuracy).


Breast tumor diagnosis DCE-MRI Texture Wavelet Classification 



The authors wish to thank Dr. Sarah Englander and Dr. Mitchell Schnall from University of Pennsylvania, USA, who supported the collection of the data. It should also be noted that K. V. Dalakleidi was supported by a scholarship for Ph.D. studies from the Hellenic State Scholarships Foundation “IKY fellowships of excellence for post-graduate studies in Greece-Siemens Program”. This work has been partially supported from the European Research Council Grant 259112.


  1. 1.
  2. 2.
    Orel, S.G., Schnall, M.D.: MR imaging of the breast for the detection, diagnosis, and staging of breast cancer. Radiology 220, 13–30 (2001)CrossRefGoogle Scholar
  3. 3.
    Schnall, M.D., et al.: Diagnostic architectural and dynamic features at breast MR imaging: multicenter study. Radiology 238, 42–53 (2006)CrossRefGoogle Scholar
  4. 4.
    Gilhuijs, K.G.A., et al.: Computerized analysis of breast lesions in three dimensions using dynamic magnetic-resonance imaging. Med. Phys. 25, 1647–1654 (1998)CrossRefGoogle Scholar
  5. 5.
    Chen, W., Giger, M.L., Bick, U., Newstead, G.M.: Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI. Med. Phys. 33, 1076–1082 (2006)Google Scholar
  6. 6.
    Lee, S.H., et al.: Optimal clustering of kinetic patterns on malignant breast lesions: comparison between K-means clustering and three-time-points method in dynamic contrast-enhanced MRI. In: Engineering in Medicine and Biology Society (2007)Google Scholar
  7. 7.
    Gibbs, P., Turnbull, L.W.: Textural analysis of contrast-enhanced MR images of the breast. Magn. Reson. Med. 50, 92–98 (2003)CrossRefGoogle Scholar
  8. 8.
    Yao, J., Chen, J., Chow, C.: Breast tumor analysis in dynamic contrast enhanced MRI using texture features and wavelet transform. IEEE J. Sel. Top. Signal Process. 3(1), 94–100 (2009)CrossRefGoogle Scholar
  9. 9.
    Agner, S.C., et al.: Textural kinetics: a novel dynamic contrast-enhanced (DCE)-MRI feature for breast lesion classification. J. Digit. Imaging 24(3), 446–463 (2010)CrossRefGoogle Scholar
  10. 10.
    Zheng, Y., et al.: STEP: spatiotemporal enhancement pattern for MR-based breast tumor diagnosis. Med. Phys. 36(7), 3192–3204 (2009)CrossRefGoogle Scholar
  11. 11.
    Gal, Y., Mehnert, A., Bradley, A., Kennedy, D., Crozier, S.: New spatiotemporal features for improved discrimination of benign and malignant lesions in dynamic contrast-enhanced magnetic resonance imaging of the breast. J. Comput. Assist. Tomogr. 35(5), 645–652 (2011)CrossRefGoogle Scholar
  12. 12.
    Tzalavra, A.G., Zacharaki, E.I., Tsiaparas, N.N., Constantinidis, F., Nikita, K.S.: A multiresolution analysis framework for breast tumor classification based on DCE-MRI. In: 2014 IEEE International Conference on Imaging Systems and Techniques (IST) Proceedings, pp. 246–250 (2014)Google Scholar
  13. 13.
    Twellmann, T., Lichte, O., Nattkemper, T.W.: An adaptive tissue characterization network for model-free visualization of dynamic contrast-enhanced magnetic resonance image data. IEEE Trans. Med. Imaging 24(10), 1256–1266 (2005)CrossRefGoogle Scholar
  14. 14.
    Mojsilovic, M., Popovic, M.V., Neskovic, A.N., Popovic, A.D.: Wavelet image extension for analysis and classification of infracted myocardial tissue. IEEE Trans. Biomed. Eng. 44(9), 856–866 (1997)CrossRefGoogle Scholar
  15. 15.
    Chen, D.R., Chang, R.F., Kuo, W.J., Chen, M.C., Huang, Y.L.: Diagnosis of breast tumors with sonographic texture analysis using wavelet transform and neural networks. Ultrasound Med. Biol. 28(10), 1301–1310 (2002)CrossRefGoogle Scholar
  16. 16.
    Tsiaparas, N.N., Golemati, S., Andreadis, I., Stoitsis, J.S., Valavanis, I., Nikita, K.S.: Comparison of multiresolution features for texture classification of carotid atherosclerosis from B-Mode ultrasound. IEEE Trans. Inf Technol. Biomed. 15(11), 130–137 (2011)CrossRefGoogle Scholar
  17. 17.
    Tsiaparas, N.N., Golemati, S., Andreadis, I., Stoitsis, J., Valavanis, I., Nikita, K.S.: Assessment of carotid atherosclerosis from B-mode ultrasound images using directional multiscale texture features. Measur. Sci. Technol. 23(11), 114004 (2012)CrossRefGoogle Scholar
  18. 18.
    Mallat, S.: Theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 674–693 (1989)CrossRefzbMATHGoogle Scholar
  19. 19.
    Furht, B.: Discrete Wavelet Transform (DWT). Encyclopedia of Multimedia. Springer, New York (2008)CrossRefGoogle Scholar
  20. 20.
  21. 21.
    Kumar, B.S., Nagaraj, S.: Discrete and stationary wavelet decomposition for IMAGE resolution enhancement. Int. J. Eng. Trends Technol. (IJETT) 4(7), 2885–2889 (2013)Google Scholar
  22. 22.
    Candès, E., Demanet, L., Donoho, D., Ying, L.: Fast discrete curvelet transforms. Multiscale Model. Simul. 5(3), 861–899 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Candes, E.J., Donoho, D.L.: Curvelets, multiresolution representation, and scaling laws. In: SPIE Proceedings, vol. 4119 (2000)Google Scholar
  24. 24.
    Witten, I.H., Frank, E., Hall, M.A.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco (2011)Google Scholar
  25. 25.
    Pearl, J.: Heuristics: Intelligent Search Strategies for Computer Problem Solving. Addison-Wesley, Reading (1984)Google Scholar
  26. 26.
    Goldberg, D.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Boston (1989)zbMATHGoogle Scholar
  27. 27.
    Manning, C.D., Raghavan, P., Schuetze, H.: An Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)CrossRefGoogle Scholar
  28. 28.
    Hartigan, J.A.: Clustering Algorithms. Wiley, New York (1975)zbMATHGoogle Scholar
  29. 29.
    Kohavi, R.: The power of decision tables. In: Lavrač, N., Wrobel, S. (eds.) ECML 1995. LNCS, vol. 912, pp. 174–189. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  30. 30.
    Landwehr, N., Hall, M., Frank, E.: Logistic model trees. Mach. Learn. 95(1–2), 161–205 (2005)CrossRefzbMATHGoogle Scholar
  31. 31.
    Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice Hall, Upper Saddle River (1999)zbMATHGoogle Scholar
  32. 32.
    John, G.H., Langley, P.: Estimating continuous distributions in Bayesian classifiers. In: Eleventh Conference on Uncertainty in Artificial Intelligence, San Mateo, pp. 338–345 (1995)Google Scholar
  33. 33.
    Lachenbruch, P.A.: Discriminant Analysis. Hafner, New York (1975)zbMATHGoogle Scholar
  34. 34.
    Zhan, T., Renping, Y., Zheng, Y., Zhan, Y., Xiao, L., Wei, Z.: Multimodal spatial-based segmentation framework for white matter lesions in multi-sequence magnetic resonance images. Biomed. Signal Process. Control 31, 52–62 (2017)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Alexia Tzalavra
    • 1
    Email author
  • Kalliopi Dalakleidi
    • 1
  • Evangelia I. Zacharaki
    • 2
  • Nikolaos Tsiaparas
    • 1
  • Fotios Constantinidis
    • 3
  • Nikos Paragios
    • 2
  • Konstantina S. Nikita
    • 1
  1. 1.School of Electrical and Computer EngineeringNational Technical University of AthensAthensGreece
  2. 2.CentraleSupélec, Inria, Université Paris-SaclaySaint-AubinFrance
  3. 3.NHS Greater Glasgow and ClydeGlasgowUK

Personalised recommendations