Circuits, Systems, and Signal Processing

, Volume 38, Issue 12, pp 5734–5754 | Cite as

Analysis of Transform-Based Features on Lateral View Breast Thermograms

  • Josephine Selle Jeyanathan
  • A. Shenbagavalli
  • B. Venkatraman
  • M. Menaka
  • J. AnithaEmail author
  • Victor Hugo C. de Albuquerque


Breast cancer is the most serious type of diseases; hence, it requires an efficient diagnosis for an early cure. Thermography for breast screening has achieved breakthroughs in detecting the abnormalities at earlier stages. In this paper, breast thermograms acquired in lateral views are considered for applying transform-based feature extraction. It is necessary to transform the acquired thermograms into the frequency domain to pick out the detailed feature that delineates abnormal from the normal ones. This paper categorizes the features under three major types of transforms namely wavelet, curvelet and contourlet transform. The transformed thermograms are then subjected to feature extraction where statistical values are measured and compared with spatial domain gray level co-occurrence matrix values. By analyzing the features using ANOVA and independent t test, the result significantly shows that the lateral view thermograms outperform than the frontal views. Finally, the features are used to classify the normal and abnormal thermograms of frontal and lateral views using pattern classifiers. According to the results, the classification performance is measured with the highest accuracy of 91%, 87% of sensitivity rate and 90% of specificity rate using the AdaBoost algorithm.


Breast thermography GLCM Wavelet transform Curvelet transform Contourlet transform ANOVA test Classifiers 



  1. 1.
    U.C. Benz et al., Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J. Photogramm. Remote sens. 58(3–4), 239–258 (2004)CrossRefGoogle Scholar
  2. 2.
    A. Criminisi, P. Pérez, K. Toyama, Region filling and object removal by exemplar-based image inpainting. IEEE Trans. Image Process. 13(9), 1200–1212 (2004)CrossRefGoogle Scholar
  3. 3.
    A.L. Da Cunha, J. Zhou, M.N. Do, The non-subsampled contourlet transform: theory, design, and applications. IEEE Trans. Image Process. 15(10), 3089–3101 (2006)CrossRefGoogle Scholar
  4. 4.
    S. Dhahbi, W. Barhoumi, E. Zagrouba, Breast cancer diagnosis in digitized mammograms using curvelet moments. Comput. Biol. Med. 64, 79–90 (2015)CrossRefGoogle Scholar
  5. 5.
    J. Dheeba, N. Albert Singh, S. Tamil Selvi, Computer-aided detection of breast cancer on mammograms: a swarm intelligence optimized wavelet neural network approach. J. Biomed. Inform. 49, 45–52 (2014)CrossRefGoogle Scholar
  6. 6.
    M.N. Do, M. Vetterli, The contourlet transform: an efficient directional multiresolution image representation. Off. J. Eur. Union Inf. Not. C 49(27A), 2091 (2006)Google Scholar
  7. 7.
    R.O. Duda, P.E. Hart, D.G. Stork, Pattern Classification (Wiley, New York, 2012)zbMATHGoogle Scholar
  8. 8.
    R.O. Duda, P.E. Hart, D.G. Stork, Pattern Classification (Wiley, New York, 2012)zbMATHGoogle Scholar
  9. 9.
    M.M. Eltoukhy, I. Faye, B.B. Samir, Breast cancer diagnosis in digital mammogram using multiscale curvelet transform. Comput. Med. Imaging Graph. 34(4), 269–276 (2010)CrossRefGoogle Scholar
  10. 10.
    M.M. Eltoukhy, I. Faye, B.B. Samir, A statistical based feature extraction method for breast cancer diagnosis in digital mammogram using multiresolution representation. Comput. Biol. Med. 42(1), 123–128 (2012)CrossRefGoogle Scholar
  11. 11.
    M. Etehadtavakol et al., Separable and non-separable discrete wavelet transform based texture features and image classification of breast thermograms. Infrared Phys. Technol. 61, 274–286 (2013)CrossRefGoogle Scholar
  12. 12.
    S.V. Francis, M. Sasikala, S. Saranya, Detection of breast abnormality from thermograms using curvelet transform based feature extraction. J. Med. Syst. 38(4), 23 (2014)CrossRefGoogle Scholar
  13. 13.
    R.M. Haralick, K. Shanmugam, Textural features for image classification. IEEE Trans. Syst. Man Cybernet. 6, 610–621 (1973)CrossRefGoogle Scholar
  14. 14.
    D.J. Hemanth, J. Anitha, V.E. Balas, Fast and accurate fuzzy C-means algorithm for MR brain image segmentation. Int J Imaging Syst Technol 26(3), 188–195 (2016)CrossRefGoogle Scholar
  15. 15.
    D.J. Hemanth, J. Anitha, B.K. Ane, Fusion of artificial neural networks for learning capability enhancement: application to medical image classification. Expert Syst (2017). CrossRefGoogle Scholar
  16. 16.
    D.J. Hemanth, J. Anitha, D.E. Popescu, L.H. Son, A modified genetic algorithm for performance improvement of transform based image steganography systems. Int J Intell Fuzzy Syst (2018). CrossRefGoogle Scholar
  17. 17.
    M. Jian, L. Liu, Texture image classification using visual perceptual texture features and gabor wavelet. J. Comput. 4(8), 763 (2009)CrossRefGoogle Scholar
  18. 18.
    J. Josephine Selle, A. Shenbagavalli, N. Sriraam, B. Venkatraman, M. Jayashree, M. Menaka, Automated recognition of ROIs for breast thermograms of lateral view-a pilot study. Quant. InfraRed Thermogr. J. 15(2), 194–213 (2018)Google Scholar
  19. 19.
    J.R. Keyserlingk et al., Functional infrared imaging of the breast. IEEE Eng. Med. Biol. Mag. 19(3), 30–41 (2000)CrossRefGoogle Scholar
  20. 20.
    I.K. Maitra, S. Nag, S.K. Bandyopadhyay, Technique for preprocessing of digital mammogram. Comput. Methods Programs Biomed. 107(2), 175–188 (2012)CrossRefGoogle Scholar
  21. 21.
    E. Micheli-Tzanakou, Supervised and Unsupervised Pattern Recognition: Feature Extraction and Computational Intelligence (CRC Press, Cambridge, 1999)CrossRefGoogle Scholar
  22. 22.
    G.H.B. Miranda, J.C. Felipe, Computer-aided diagnosis system based on fuzzy logic for breast cancer categorization. Comput. Biol. Med. 64, 334–346 (2015)CrossRefGoogle Scholar
  23. 23.
    F. Moayedi et al., Contourlet-based mammography mass classification using the SVM family. Comput. Biol. Med. 40(4), 373–383 (2010)CrossRefGoogle Scholar
  24. 24.
    E.Y.-K. Ng, A review of thermography as promising non-invasive detection modality for breast tumor. Int. J. Therm. Sci. 48(5), 849–859 (2009)CrossRefGoogle Scholar
  25. 25.
    F. Pak, H.R. Kanan, A. Alikhassi, Breast cancer detection and classification in digital mammography based on Non-Subsampled Contourlet Transform (NSCT) and Super Resolution. Comput. Methods Programs Biomed. 122(2), 89–107 (2015)CrossRefGoogle Scholar
  26. 26.
    P.P. Rebouças Filho, S.A. Peixoto, R.V. Medeiros da Nóbrega, D.J. Hemanth, A.G. Medeiros, A.K. Sangaiah, V.H.C. de Albuquerque, Automatic histologically-closer classification of skin lesions. Comput. Med. Imaging Graph. 68, 40–54 (2018)CrossRefGoogle Scholar
  27. 27.
    S.S. Suganthi, S. Ramakrishnan, Analysis of breast thermograms using gabor wavelet anisotropy index. J. Med. Syst. 38(9), 101 (2014)CrossRefGoogle Scholar
  28. 28.
    Y. Xiang et al., Quantification of trabecular bone mass and orientation using Gabor wavelets, in Proceedings of the 2003 ACM Symposium on Applied Computing. ACM (2003)Google Scholar
  29. 29.
    B. Zheng et al., Improvement of visual similarity of similar breast masses selected by computer-aided diagnosis schemes, in 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2007. ISBI 2007. IEEE (2007)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Josephine Selle Jeyanathan
    • 1
  • A. Shenbagavalli
    • 2
  • B. Venkatraman
    • 3
  • M. Menaka
    • 4
  • J. Anitha
    • 5
    Email author
  • Victor Hugo C. de Albuquerque
    • 6
  1. 1.Department of ECEKalasalingam Academy of Research and EducationKrishnankoilIndia
  2. 2.Department of ECENational Engineering CollegeKovilpattiIndia
  3. 3.Radiological Safety DivisionIndira Gandhi Centre for ResearchKalpakkamIndia
  4. 4.Quality Assurance DivisionIndira Gandhi Centre for ResearchKalpakkamIndia
  5. 5.Department of ECEKarunya Institute of Technology and SciencesCoimbatoreIndia
  6. 6.Programa de Pós-Graduação em Informática AplicadaUniversidade de FortalezaFortalezaBrazil

Personalised recommendations