Multiple Classifier Systems in Texton-Based Approach for the Classification of CT Images of Lung

  • Mehrdad J. Gangeh
  • Lauge Sørensen
  • Saher B. Shaker
  • Mohamed S. Kamel
  • Marleen de Bruijne
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6533)


In this paper, we propose using texton signatures based on raw pixel representation along with a parallel multiple classifier system for the classification of emphysema in computed tomography images of the lung. The multiple classifier system is composed of support vector machines on the texton signatures as base classifiers and combines their decisions using product rule. The proposed approach is tested on 168 annotated regions of interest consisting of normal tissue, centrilobular emphysema, and paraseptal emphysema. Texton-based approach in texture classification mainly has two parameters, i.e., texton size and k value in k-means. Our results show that while aggregation of single decisions by SVMs over various k values using multiple classifier systems helps to improve the results compared to single SVMs, combining over different texton sizes is not beneficial. The performance of the proposed system, with an accuracy of 95%, is similar to a recently proposed approach based on local binary patterns, which performs almost the best among other approaches in the literature.


Support Vector Machine Compute Tomography Image Local Binary Pattern Filter Bank Feature Subset 
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.


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  1. 1.
    Uppaluri, R., Mitsa, T., Sonka, M., Hoffman, E.A., McLennan, G.: Quantification of Pulmonary Emphysema from Lung Computed Tomography Images. Amer. J. Respir. Crit. Care Med. 156(1), 248–254 (1997)CrossRefGoogle Scholar
  2. 2.
    Sluimer, I.C., Prokop, M., Hartmann, I., van Ginneken, B.: Automated Classification of Hyperlucency, Fibrosis, Ground Glass, Solid, and Focal Lesions in High-Resolution CT of the Lung. Medical Physics 33(7), 2610–2620 (2006)CrossRefGoogle Scholar
  3. 3.
    Chabat, F., Yang, G.Z., Hansell, D.M.: Obstructive Lung Diseases: Texture Classification for Differentiation at CT. Radiology 228(3), 871–877 (2003)CrossRefGoogle Scholar
  4. 4.
    Xu, Y., Sonka, M., McLennan, G., Guo, J., Hoffman, E.A.: MDCT-based 3-D Texture Classification of Emphysema and Early Smoking Related Lung Pathologies. IEEE Trans. Med. Imag. 25(4), 464–475 (2006)CrossRefGoogle Scholar
  5. 5.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Trans. Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)CrossRefMATHGoogle Scholar
  6. 6.
    Varma, M., Zisserman, A.: A Statistical Approach to Material Classification Using Image Patch Exemplars. IEEE Trans. Pattern Analysis and Machine Intelligence 31(11), 2032–2047 (2009)CrossRefGoogle Scholar
  7. 7.
    Caputo, B., Hayman, E., Fritz, M., Eklundh, J.O.: Classifying Materials in the Real World. Image and Vision Computing 28(1), 150–163 (2010)CrossRefGoogle Scholar
  8. 8.
    Varma, M., Zisserman, A.: A Statistical Approach to Texture Classification from Single Images. International Journal of Computer Vision: Special Issue on Texture Analysis and Synthesis 62(1-2), 61–81 (2005)CrossRefGoogle Scholar
  9. 9.
    Gangeh, M.J., Sørensen, L., Shaker, S.B., Kamel, M.S., de Bruijne, M., Loog, M.: A Texton-Based Approach for the Classification of Lung Parenchyma in CT Images. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6363, pp. 596–603. Springer, Heidelberg (2010)Google Scholar
  10. 10.
    Garcia, M.A., Puig, D.: Supervised Texture Classification by Integration of Multiple Texture Methods and Evaluation Windows. Image and Vision Computing 25(7), 1091–1106 (2007)CrossRefGoogle Scholar
  11. 11.
    Kuncheva, L.I.: Combining Pattern Classifiers Methods and Algorithms. John Wiley & Sons, New Jersey (2004)CrossRefMATHGoogle Scholar
  12. 12.
    Lanckriet, G.R.G., Cristianini, N., Bartlett, P., Ghaoui, L.E., Jordan, M.I.: Learning the Kernel Matrix with Semidefinite Programming. Journal of Machine Learning Research 5(1), 27–72 (2005)MathSciNetMATHGoogle Scholar
  13. 13.
    Bach, F.R., Lanckriet, G.R.G., Jordan, M.I.: Multiple Kernel Learning, Conic Duality, and the SMO Algorithm. In: Proceedings of 21st International Conference of Machine Learning, ICML (2004)Google Scholar
  14. 14.
    Julesz, B.: Textons, the Elements of Texture Perception, and Their Interactions. Nature 290(5802), 91–97 (1981)CrossRefGoogle Scholar
  15. 15.
    Leung, T., Malik, J.: Representing and Recognizing the Visual Appearance of Materials Using Three-Dimensional Textons. International Journal of Computer Vision 43(1), 29–44 (2001)CrossRefMATHGoogle Scholar
  16. 16.
    Cula, O.G., Dana, K.J.: 3D Texture Recognition Using Bidirectional Feature Histograms. International Journal of Computer Vision 59(1), 33–60 (2004)CrossRefGoogle Scholar
  17. 17.
    Fan, R.E., Chen, P.H., Lin, C.J.: Working Set Selection Using the Second Order Information for Training SVM. Journal of Mach. Learning Research 6, 1889–1918 (2005)MATHGoogle Scholar
  18. 18.
    Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On Combining Classifiers. IEEE Trans. Pattern Analysis and Machine Intelligence 20(3), 226–239 (1998)CrossRefGoogle Scholar
  19. 19.
    Webb, W.R., Müller, N., Naidich, D.: High-Resolution CT of the Lung, 3rd edn. Lippincott Williams & Wilkins (2001)Google Scholar
  20. 20.
    Sørensen, L., Shaker, S.B., de Bruijne, M.: Texture Classification in Lung CT Using Local Binary Patterns. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 934–941. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  21. 21.
    Sørensen, L., Shaker, S.B., de Bruijne, M.: Quantitative Analysis of Pulmonary Emphysema Using Local Binary Patterns. IEEE Trans. Med. Imag. 29(2), 559–569 (2010)CrossRefGoogle Scholar
  22. 22.
    Ho, T.K.: The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8), 832–844 (1998)CrossRefGoogle Scholar
  23. 23.
    Tuzel, O., Yang, L., Meer, P., Foran, D.J.: Classification of Hematologic Malignancies Using Texton Signatures. Pattern Analysis and Applications 10(4), 277–290 (2007)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Zhong, C., Sun, Z., Tan, T.: Robust 3D Face Recognition Using Learned Visual Codebook. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2007), pp. 1–6 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mehrdad J. Gangeh
    • 1
  • Lauge Sørensen
    • 2
  • Saher B. Shaker
    • 3
  • Mohamed S. Kamel
    • 1
  • Marleen de Bruijne
    • 2
    • 4
  1. 1.Department of Electrical and Computer EngineeringUniversity of WaterlooCanada
  2. 2.Department of Computer ScienceUniversity of CopenhagenDenmark
  3. 3.Department of Respiratory MedicineGentofte University HospitalHellerupDenmark
  4. 4.Biomedical Imaging Group RotterdamErasmus MCThe Netherlands

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