MRI Texture-Based Classification of Dystrophic Muscles. A Search for the Most Discriminative Tissue Descriptors

  • Dorota DudaEmail author
  • Marek Kretowski
  • Noura Azzabou
  • Jacques D. de Certaines
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9842)


The study assesses the usefulness of various texture-based tissue descriptors in the classification of canine hindlimb muscles. Experiments are performed on T2-weighted Magnetic Resonance Images (MRI) acquired from healthy and Golden Retriever Muscular Dystrophy (GRMD) dogs over a period of 14 months. Three phases of canine growth and/or dystrophy progression are considered. In total, 39 features provided by 8 texture analysis methods are tested. Features are ranked according to their frequency of selection in a modified Monte Carlo procedure. The top-ranked features are used in differentiation (i) between GRMD and healthy dogs at each phase of canine growth, and (ii) between three phases of dystrophy progression in GRMD dogs. Three classifiers are applied: Adaptive Boosting, Neural Networks, and Support Vector Machines. Small sets of selected features (up to 10) are found to ensure highly satisfactory classification accuracies.


Golden Retriever Muscular Dystrophy (GRMD) Duchenne Muscular Dystrophy (DMD) Texture analysis Feature selection Classification MRI T2 



This work was performed under the auspices of the European COST Action BM1304, MYO-MRI. It was also performed in the framework of the grant S/WI/2/2013 (Bialystok University of Technology), founded by the Polish Ministry of Science and Higher Education.


  1. 1.
    Haldeman-Englert, C.: Duchenne Muscular Dystrophy: MedlinePlus Medical Encyclopedia. Medline Plus. U.S. National Library of Medicine (2014).
  2. 2.
    Sarnat, H.B.: Muscular dystrophies. In: Kliegman, R.M., Stanton, B.F., Geme, J.W., Schor, N.F., Behrman, R.E. (eds.) Nelson Textbook of Pediatrics, 19th edn. Saunders Elsevier, Philadelphia (2011)Google Scholar
  3. 3.
    Kornegay, J.N., Bogan, J.R., Bogan, D.J., Childers, M.K., Li, J., et al.: Canine models of Duchenne muscular dystrophy and their use in therapeutic strategies. Mamm. Genome 23(1–2), 85–108 (2012)CrossRefGoogle Scholar
  4. 4.
    De Certaines, J.D., Larcher, T., Duda, D., Azzabou, N., Eliat, P.A., et al.: Application of texture analysis to muscle MRI: 1-What kind of information should be expected from texture analysis? EPJ Nonlinear Biomed. Phys. 3(3), 1–14 (2015)Google Scholar
  5. 5.
    Lerski, R.A., de Certaines, J.D., Duda, D., Klonowski, W., Yang, G., et al.: Application of texture analysis to muscle MRI: 2-Technical recommendations. EPJ Nonlinear Biomed. Phys. 3(2), 1–20 (2015)Google Scholar
  6. 6.
    Castellano, G., Bonilha, L., Li, L.M., Cendes, F.: Texture analysis of medical images. Clin. Radiol. 59(12), 1061–1069 (2004)CrossRefGoogle Scholar
  7. 7.
    Hajek, M., Dezortova, M., Materka, A., Lerski, R.A. (eds.): Texture Analysis for Magnetic Resonance Imaging. Med4Publishing, Prague (2006)Google Scholar
  8. 8.
    Nailon, W.H.: Texture analysis methods for medical image characterisation. In: Mao, Y. (ed.) Biomedical Imaging, pp. 75–100. InTech Open (2010)Google Scholar
  9. 9.
    Duda, D., Kretowski, M., Azzabou, N., de Certaines, J.D.: MRI texture analysis for differentiation between healthy and golden retriever muscular dystrophy dogs at different phases of disease evolution. In: Saeed, K., Homenda, W. (eds.) CISIM 2015. LNCS, vol. 9339, pp. 255–266. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  10. 10.
    Draminski, M., Rada-Iglesias, A., Enroth, S., Wadelius, C., Koronacki, J., Komorowski, J.: Monte Carlo feature selection for supervised classification. Bioinformatics 24(1), 110–117 (2008)CrossRefGoogle Scholar
  11. 11.
    Fan, Z., Wang, J., Ahn, M., Shiloh-Malawsky, Y., Chahin, N., et al.: Characteristics of magnetic resonance imaging biomarkers in a natural history study of golden retriever muscular dystrophy. Neuromuscul. Disord. 24(2), 178–191 (2014)CrossRefGoogle Scholar
  12. 12.
    Galloway, M.M.: Texture analysis using gray level run lengths. Comput. Graph. Image Process. 4(2), 172–179 (1975)CrossRefGoogle Scholar
  13. 13.
    Yang, G., Lalande, V., Chen, L., Azzabou, N., Larcher, T., et al.: MRI texture analysis of GRMD dogs using orthogonal moments: a preliminary study. IRBM 36(4), 213–219 (2015)CrossRefGoogle Scholar
  14. 14.
    Vapnik, V.N.: The Nature of Statistical Learning Theory, 2nd edn. Springer, New York (2000)zbMATHCrossRefGoogle Scholar
  15. 15.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. Syst. SMC–3(6), 610–621 (1973)CrossRefGoogle Scholar
  16. 16.
    Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)zbMATHGoogle Scholar
  17. 17.
    Thibaud, J.L., Azzabou, N., Barthelemy, I., Fleury, S., Cabrol, L., et al.: Comprehensive longitudinal characterization of canine muscular dystrophy by serial NMR imaging of GRMD dogs. Neuromuscul. Disord. 22(Suppl. 2), S85–S99 (2012)CrossRefGoogle Scholar
  18. 18.
    Duda, D.: Medical image classification based on texture analysis. Ph.D. thesis, University of Rennes 1, Rennes, France (2009)Google Scholar
  19. 19.
    Lerski, R., Straughan, K., Shad, L., Boyce, D., Bluml, S., Zuna, I.: MR image texture analysis - an approach to tissue characterization. Magn. Reson. Imaging 11(6), 873–887 (1993)CrossRefGoogle Scholar
  20. 20.
    Weszka, J.S., Dyer, C.R., Rosenfeld, A.: A comparative study of texture measures for terrain classification. IEEE Trans. Syst. Man Cybern. 6(4), 269–285 (1976)zbMATHCrossRefGoogle Scholar
  21. 21.
    Laws, K.I.: Textured image segmentation. Ph.D. thesis, University of Southern California, Los Angeles, CA, USA (1980)Google Scholar
  22. 22.
    Chen, E.L., Chung, P.C., Chen, C.L., Tsai, H.M., Chang, C.I.: An automatic diagnostic system for CT liver image classification. IEEE Trans. Biomed. Eng. 45(6), 783–794 (1998)CrossRefGoogle Scholar
  23. 23.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Addison-Wesley, Reading (2002)Google Scholar
  24. 24.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. 11(1), 10–18 (2009)CrossRefGoogle Scholar
  25. 25.
    Quinlan, J.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)Google Scholar
  26. 26.
    Freund, Y., Shapire, R.: A decision-theoretic generalization of online learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)CrossRefGoogle Scholar
  27. 27.
    Rojas, R.: Neural Networks. A Systematic Introduction. Springer, Berlin (1996)zbMATHGoogle Scholar
  28. 28.
    Platt, J.C.: Fast training of support vector machines using sequential minimal optimization. In: Scholkopf, B., Burges, C.J.C., Smola, A.J. (eds.) Advances in Kernel Methods - Support Vector Learning, pp. 185–208. MIT Press, Cambridge (1998)Google Scholar

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Authors and Affiliations

  • Dorota Duda
    • 1
    Email author
  • Marek Kretowski
    • 1
  • Noura Azzabou
    • 2
    • 3
  • Jacques D. de Certaines
    • 2
    • 3
  1. 1.Faculty of Computer ScienceBialystok University of TechnologyBialystokPoland
  2. 2.Institute of Myology, Nuclear Magnetic Resonance LaboratoryParisFrance
  3. 3.CEA, I2BM, MIRCen, NMR LaboratoryParisFrance

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