Automatic Recognition of Microcalcifications in Mammography Images through Fractal Texture Analysis

  • Hernán Darío Vargas Cardona
  • Álvaro Orozco
  • Mauricio A. Álvarez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8888)


Mammography images are widely used for detection of microcalcifications (MCs), which constitute the early stage of breast cancer. Moreover, these images allow the medical specialist to perform a timely diagnosis and to prevent complications in patients. Automatic identification of MCs in mammography images may be useful as a decision support given by a specialist. In this paper, we construct a mammography image database with medical validation and expert labeling. The test subjects are from a local population located in the Eje cafetero, Colombia. Also, we present a methodology for automatic recognition of microcalcifications based on segmentation with fractal texture analysis (SFTA) and a support vector machine (SVM). For a comparison framework with the state of the art, we compare our methodology with the local binary patterns (LBP) method, that is widely applied in digital images processing. Results show that SFTA methodology for recognition of MCs achieves an accuracy over 92.5% improving significatively when compared to LBP. Also, our database satisfies the epidemiological parameters to represent a local population.


Support Vector Machine Local Binary Pattern Digital Mammography Automatic Recognition Mammography Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Murray, C., López, A.: Mortality by cause for eight regions of the world: Global Burden of disease study. Lancet 349, 1269–1279 (1997)CrossRefGoogle Scholar
  2. 2.
    Angarita, F., Acuna, A.: Cncer de seno: de la epidemiología al tratamiento. Univerisdad Javeriana 349, 344–372 (2008)Google Scholar
  3. 3.
    Sukhamwang, N., Muttarak, M., Gravano, L., Kongmebhol, P.: Breast calcifications: which are malignant? Singapore Med J. 50, 907–913 (2009)Google Scholar
  4. 4.
    Sucklin, J., Parker, J., Dance, D., Astley, S., Hutt, I., Boggis, C., Ricketts, I., Stamatakis, E., Cerneaz, N., Kok, S., Taylor, P., Betal, D., Savage, J.: The mammographic images analysis society digital mammogram database. In: International Congress Series, pp. 375–378 (1994)Google Scholar
  5. 5.
    Heath, M., Bowyer, K., Kopans, D., Kegelmeyer, P.J., 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. Computational Imaging and Vision, vol. 13, pp. 457–460. Springer Netherlands (1998)Google Scholar
  6. 6.
    Sameti, M., Ward, R.K., Morgan-Parkes, J., Palcic, B.: Image feature extraction in the last screening mammograms prior to detection of breast cancer. IEEE Journal of Selected Topics in Signal Processing 3, 46–52 (2009)CrossRefGoogle Scholar
  7. 7.
    Papadopoulos, A., Fotiadis, D., Costaridou, L.: Improvement of microcalcification cluster detection in mammography utilizing image enhancement techniques. Computers in Biology and Medicine 38, 1045–1055 (2008)CrossRefGoogle Scholar
  8. 8.
    Bovik, A. (ed.): Handbook of Image and Video Processing, vol. 2. Academic Press (2005)Google Scholar
  9. 9.
    Zhang, X.S., Xie, H.: Discriminant subspace learning for microcalcification clusters detection. Physics Procedia 24, Part C, 2237–2244 (2012), International Conference on Applied Physics and Industrial Engineering 2012Google Scholar
  10. 10.
    Zhang, X., Gao, X.: Twin support vector machines and subspace learning methods for microcalcification clusters detection. Engineering Applications of Artificial Intelligence 25, 1062–1072 (2012)CrossRefGoogle Scholar
  11. 11.
    Tiedeu, A., Daul, C., Kentsop, A., Graebling, P., Wolf, D.: Texture-based analysis of clustered microcalcifications detected on mammograms. Digital Signal Processing 22, 124–132 (2012)CrossRefMathSciNetGoogle Scholar
  12. 12.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 971–987 (2002)CrossRefGoogle Scholar
  13. 13.
    Oliver, A., Lladó, X., Freixenet, J., Martí, J.: False positive reduction in mammographic mass detection using local binary patterns. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 286–293. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  14. 14.
    Berment, H., Becette, V., Mohallem, M., Ferreira, F., Chérel, P.: Les masses en mammographie: quelles lésions anatomopathologiques sous-jacentes. Journal de Radiologie Diagnostique et Interventionnelle 95, 126–135 (2014)CrossRefGoogle Scholar
  15. 15.
    Costa, A., Humpire-Mamani, G., Traina, A.J.M.: An efficient algorithm for fractal analysis of textures. In: 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 39–46 (August 2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Hernán Darío Vargas Cardona
    • 1
  • Álvaro Orozco
    • 1
  • Mauricio A. Álvarez
    • 1
  1. 1.Department of Electric EngineeringUniversidad Tecnológica de PereiraPereiraColombia

Personalised recommendations