Advances in Texture Analysis for Emphysema Classification

  • Rodrigo Nava
  • J. Victor Marcos
  • Boris Escalante-Ramírez
  • Gabriel Cristóbal
  • Laurent U. Perrinet
  • Raúl San José Estépar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8259)


In recent years, with the advent of High-resolution Computed Tomography (HRCT), there has been an increased interest for diagnosing Chronic Obstructive Pulmonary Disease (COPD), which is commonly presented as emphysema. Since low-attenuation areas in HRCT images describe different emphysema patterns, the discrimination problem should focus on the characterization of both local intensities and global spatial variations. We propose a novel texture-based classification framework using complex Gabor filters and local binary patterns. We also analyzed a set of global and local texture descriptors to characterize emphysema morphology. The results have shown the effectiveness of our proposal and that the combination of descriptors provides robust features that lead to an improvement in the classification rate.


Co-occurrence matrices Emphysema Gabor filters LBP Sparsity Tchebichef Texture analysis 


  1. 1.
    Galban, C., Han, M., Boes, J., Chughtai, K., Meyer, C., Johnson, T., Galban, S., Rehemtulla, A., Kazerooni, E., Martínez, F., Ross, B.: Computed tomography-based biomarker provides unique signature for diagnosis of COPD phenotypes and disease progression. Nat. Med. 18(11), 1711–1715 (2012)CrossRefGoogle Scholar
  2. 2.
    Hayhurst, M., Flenley, D., Mclean, A., Wightman, A., Macnee, W., Wright, D., Lamb, D., Best, J.: Diagnosis of pulmonary emphysema by computerised tomography. The Lancet 324, 320–322 (1984)CrossRefGoogle Scholar
  3. 3.
    Sørensen, L., Shaker, S., de Bruijne, M.: Quantitative analysis of pulmonary emphysema using Local Binary Patterns. IEEE Trans. Med. Imag. 29(2), 559–569 (2010)CrossRefGoogle Scholar
  4. 4.
    Mendoza, C., Washko, G., Ross, J., Diaz, A., Lynch, D., Crapo, J., Silverman, E., Acha, B., Serrano, C., Estepar, R.: Emphysema quantification in a multi-scanner HRCT cohort using local intensity distributions. In: 9th IEEE International Symposium on Biomedical Imaging (ISBI), pp. 474–477 (2012)Google Scholar
  5. 5.
    Sørensen, L., Nielsen, M., Lo, P., Ashraf, H., Pedersen, J., de Bruijne, M.: Texture-based analysis of COPD: A data-driven approach. IEEE Trans. Med. Imag. 31(1), 70–78 (2012)CrossRefGoogle Scholar
  6. 6.
    Depeursinge, A., Foncubierta–Rodriguez, A., Van de Ville, D., Müller, H.: Multiscale lung texture signature learning using the riesz transform. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 517–524. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  7. 7.
    Gabor, D.: Theory of communication. J. Inst. Elec. Eng (London) 93III, 429–457 (1946)Google Scholar
  8. 8.
    Daugman, J.: Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. J. Opt. Soc. Am. A 2, 1160–1169 (1985)CrossRefGoogle Scholar
  9. 9.
    Nava, R., Escalante-Ramírez, B., Cristóbal, G.: Texture image retrieval based on log-gabor features. In: Alvarez, L., Mejail, M., Gomez, L., Jacobo, J. (eds.) CIARP 2012. LNCS, vol. 7441, pp. 414–421. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  10. 10.
    Field, D.: Relations between the statistics of natural images and the response properties of cortical cells. J. Opt. Soc. Am. A 4(12), 2379–2394 (1987)CrossRefGoogle Scholar
  11. 11.
    Perrinet, L.U., Samuelides, M., Thorpe, S.J.: Sparse spike coding in an asynchronous feed-forward multi-layer neural network using matching pursuit. Neurocomputing 57C (2002)Google Scholar
  12. 12.
    Perrinet, L.U.: Role of homeostasis in learning sparse representations. Neural Computation 22(7), 1812–1836 (2010)CrossRefzbMATHGoogle Scholar
  13. 13.
    Haralick, R., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst., Man, Cybern., Syst. SMC-3(6), 610–621 (1973)CrossRefGoogle Scholar
  14. 14.
    Randen, T., Husøy, J.H.: Filtering for texture classification: A comparative study. IEEE Trans. Pattern Anal. Mach. Intell. 21, 291–310 (1999)CrossRefGoogle Scholar
  15. 15.
    Marcos, V., Cristóbal, G.: Texture classification using Tchebichef moments. J. Opt. Soc. Am. A 30(8), 1580–1591 (2013)CrossRefGoogle Scholar
  16. 16.
    Ojala, T., Pietikainen, M., Harwood, D.: Performance evaluation of texture measures with classification based on Kullback discrimination of distributions. In: 12th International Conference on Pattern Recognition - Conference A: Computer Vision Image Processing (IAPR), vol. 1, pp. 582–585 (1994)Google Scholar
  17. 17.
    Ojala, T., Pietikäinen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with Local Binary Patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRefGoogle Scholar
  18. 18.
    Mika, S., Ratsch, G., Weston, J., Scholkopf, B., Mullers, K.: Fisher discriminant analysis with kernels. In: IEEE Signal Processing Society Workshop on Neural Networks for Signal Processing IX, pp. 41–48 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Rodrigo Nava
    • 1
  • J. Victor Marcos
    • 2
  • Boris Escalante-Ramírez
    • 1
  • Gabriel Cristóbal
    • 2
  • Laurent U. Perrinet
    • 3
  • Raúl San José Estépar
    • 4
  1. 1.Posgrado en Ciencia e Ingeniería de la ComputaciónUniversidad Nacional Autónoma de MéxicoMexico CityMexico
  2. 2.Instituto de Óptica, Spanish National Research Council (CSIC)MadridSpain
  3. 3.INCM, UMR6193, CNRSAix-Marseille UniversityMarseille Cedex 20France
  4. 4.Harvard Medical SchoolBrigham and Women’s HospitalBostonUnited States

Personalised recommendations