Tsallis Entropy Extraction for Mammographic Region Classification

  • Rafaela Alcântara
  • Perfilino Ferreira Junior
  • Aline Ramos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10125)


Breast cancer is the second disease responsible for women’s death in the world. To reduce the number of cases, screening mammography is used to detect this disease. To improve exam accuracy results, computer-aided systems (CAD) have been developed to analyze the mammography and provide statistics based on image features extracted. This paper presents a novel approach for a computer-aided detection system (CADe) based on Tsallis entropy extraction from quantized gray level co-occurrence matrix (GLCM) from mass images. A comparison study is presented based on a feature extraction scheme using weigthed Haralick features. The best result accuracy rate was 91.3% from Tsallis entropy based on GLCM matrix using 24 feature measures.


Tsallis entropy Mammography Classification SVM Haralick features 


  1. 1.
    Globocan Cancer Fact Sheets: Breast Cancer. http://globocan.iarc.fr/Default.aspx. Accessed 03 July 2015
  2. 2.
    Jalalian, A., Mashohor, S.B., Mahmud, H.R., Saripan, M.I.B., Ramli, A.R.B., Karasfi, B.: Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review. Clin. Imaging 37(3), 420–426 (2013)CrossRefGoogle Scholar
  3. 3.
    Mavroforakis, M., Georgiou, H., Cavouras, D., Dimitropoulos, N., Theodoridis, S.: Mammographic mass classification using textural features and descriptive diagnostic data. In: 2002 14th International Conference on Digital Signal Processing, DSP 2002, vol. 1, pp. 461–464 (2002)Google Scholar
  4. 4.
    Martins, L., Junior, G.B., Silva, A.C., de Paiva, A.C., Gattass, M.: Detection of masses in digital mammograms using k-means and support vector machine. ELCVIA: Electron. Lett. Comput. Vis. Image Anal. 8(2), 39–50 (2009)Google Scholar
  5. 5.
    Haralick, R., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 6, 610–621 (1973)CrossRefGoogle Scholar
  6. 6.
    Mohanalin, B., Kalra, P.K., Kumar, N.: A novel automatic microcalcification detection technique using Tsallis entropy a type II fuzzy index. Comput. Math. Appl. 60(8), 2426–2432 (2010)CrossRefGoogle Scholar
  7. 7.
    Chang, C.-C., Lin, C.-J.: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011)CrossRefGoogle Scholar
  8. 8.
    Heath, M., Bowyer, K., Kopans, D., Kegelmeyer, P., Moore, R., Chang, K., Munishkumaran, S.: Current status of the digital database for screening mammography. In: Karssemeijer, N., Thijssen, M., Hendriks, J., van Erning, L. (eds.) Digital Mammography, vol. 13, pp. 457–460. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  9. 9.
    Garcia-Manso, A., Garcia-Orellana, C.J., Gonzalez-Velasco, H., Gallardo-Caballero, R., Macias Macias, M.: Consistent performance measurement of a system to detect masses in mammograms based on blind feature extraction. BioMed. Eng. Online 12, 2–18 (2013)CrossRefGoogle Scholar
  10. 10.
    Mata, B., Meenaksh, M.: A novel approach for automatic detection of abnormalities in mammograms. In: 2011 IEEE Recent Advances in Intelligent Computational Systems (RAICS), pp. 831–836 (2011)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Rafaela Alcântara
    • 1
  • Perfilino Ferreira Junior
    • 1
  • Aline Ramos
    • 1
  1. 1.Computer Science Departament, Mathematics InstituteFederal University of BahiaSalvadorBrazil

Personalised recommendations