Texture Based Segmentation

  • Reyer Zwiggelaar
  • Erika R. E. Denton
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4046)


The ability of human observers to discriminate between textures is related to the contrast between key structural elements and their repeating patterns. Here we have developed an automatic texture classification approach based on this principle. Local contrast information is modelled and a hybrid metric, based on probability density distributions and transportation estimation, are used to classify unseen samples. Quantitative and qualitative evaluation, based on mammographic images and Wolfe classification, is presented and shows segmentation results in line with the various classes.


Binary Image Mammographic Density Segmentation Result Local Binary Pattern Probability Density Distribution 
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.
    Haralick, M.W.: Statistical and structural approaches to texture. Proceedings of the IEEE 67(5), 786–804 (1979)CrossRefGoogle Scholar
  2. 2.
    Conners, R.W., Harlow, C.A.: A theoretical comparison of texture algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 2(3), 204–222 (1980)MATHCrossRefGoogle Scholar
  3. 3.
    Pentland, A.P.: Fractal-based description of natural scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence 6(6), 661–674 (1984)CrossRefGoogle Scholar
  4. 4.
    Reed, T.R., Dubuf, J.M.H.: A review of recent texture segmentation and feature-extraction techniques. Computer Vision, Graphics and Image Processing 57(3), 359–372 (1993)CrossRefGoogle Scholar
  5. 5.
    Reyes-Aldasoro, C.C., Bhalerao, A.: Volumetric texture description and discrimant feature selection for MRI. In: Taylor, C.J., Noble, J.A. (eds.) IPMI 2003. LNCS, vol. 2732, pp. 282–293. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  6. 6.
    Zwiggelaar, R.: Texture based segmentation: automatic selection of co-occurrence matrices. In: 17th IEEE International Conference on Pattern Recognition, pp. 588–591 (2004)Google Scholar
  7. 7.
    Varma, M., Zisserman, A.: Classifying images of materials: achieving viewpoint and illumination independence. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 255–271. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  8. 8.
    Chantler, M., Van Gool, L.: Special issue on texture analysis and synthesis. International Journal of Computer Vision 62, 5 (2005)Google Scholar
  9. 9.
    Pietikainen, M.: Image analysis with local binary patterns. In: Kalviainen, H., Parkkinen, J., Kaarna, A. (eds.) SCIA 2005. LNCS, vol. 3540, pp. 115–118. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    Wolfe, J.N.: Risk for breast cancer development determined by mammographic parenchymal pattern. Cancer 37(5), 2486–2492 (1976)CrossRefGoogle Scholar
  11. 11.
    Gram, I.T., Funkhouser, E., Tabar, L.: The tabar classification of mammographic parenchymal patterns. European Journal of Radiology 24(2), 131–136 (1997)CrossRefGoogle Scholar
  12. 12.
    Hitchcock, F.L.: The distribution of a product from several sources to numerous localities. Journal of Mathematical Physics 20, 224–230 (1941)MATHMathSciNetGoogle Scholar
  13. 13.
    Rubner, Y., Tomasi, C., Guibas, L.J.: A metric for distributions with applications to image databases. In: 6th International Conference on Computer Vision, pp. 59–66 (1998)Google Scholar
  14. 14.
    Giannopoulos, P., Veltkamp, R.C.: A pseudo-metric for weighted point sets. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 715–730. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  15. 15.
    Holmes, A.S., Rose, C.J., Taylor, C.J.: Measuring similarity between pixel signatures. Image and Vision Computing 20(5-6), 331–340 (2002)CrossRefGoogle Scholar
  16. 16.
    Haker, S., Zhu, L., Tannenbaum, A., Angenent, S.: Optimal mass transport for registration and warping. International Journal of Computer Vision 60(3), 225–240 (2004)CrossRefGoogle Scholar
  17. 17.
    Jing, F., Li, M.J., Zhang, H.J., Zhang, B.: An efficient and effective region-based image retrieval framework. IEEE Transactions on Image Processing 13(5), 699–709 (2004)CrossRefGoogle Scholar
  18. 18.
    Hall, F.M.: Mammographic density categories. American Journal of Radiology 178, 242–242 (2002)Google Scholar
  19. 19.
    Venta, L.A., Hendrick, R.E.: Mammographic density categories - reply. American Journal of Radiology 178, 242–243 (2002)Google Scholar
  20. 20.
    Karssemeijer, N.: Automated classification of parenchymal patterns in mammograms. Phys. Med. Biol. 43, 365–378 (1998)CrossRefGoogle Scholar
  21. 21.
    Byng, J.W., Yaffe, M.J., Lockwood, G.A., Little, L.E., Tritchler, D.L., Boyd, N.F.: Automated analysis of mammographic densities and breast carcinoma risk. Cancer 80(1), 66–74 (1997)CrossRefGoogle Scholar
  22. 22.
    Zwiggelaar, R., Denton, E.R.E.: Optimal segmentation of mammographic images. In: 7th International Workshop on Digital Mammography (2004) (to be published)Google Scholar
  23. 23.
    Smith, S.M., Brady, J.M.: Susan – a new approach to low level image processing. International Journal of Computer Vision 23(1), 45–78 (1997)CrossRefGoogle Scholar
  24. 24.
    Geman, S., Geman, D.: Stochastic relaxation, Gibbs distribution, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence 6, 721–741 (1984)MATHCrossRefGoogle Scholar
  25. 25.
    Gimel’farb, G.L.: Texture modeling by multiple pairwise pixel interactions. IEEE Transactions on Pattern Analysis and Machine Intelligence 18, 1110–1114 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Reyer Zwiggelaar
    • 1
  • Erika R. E. Denton
    • 2
  1. 1.Department of Computer ScienceUniversity of WalesAberystwythUK
  2. 2.Norfolk and Norwich University HospitalNorwichUK

Personalised recommendations