On Different Colour Spaces for Medical Colour Image Classification

  • Cecilia Di Ruberto
  • Giuseppe Fodde
  • Lorenzo PutzuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9256)


Analysis of cells and tissues allow the evaluation and diagnosis of a vast number of diseases. Nowadays this analysis is still performed manually, involving numerous drawbacks, in particular the results accuracy heavily depends on the operator skills. Differently, the automated analysis by computer is performed quickly, requires only one image of the sample and provides precise results. In this work we investigate different texture descriptors extracted from medical images in different colour spaces. We compare these features in order to identify the features set able to properly classify medical images presenting different classification problems. Furthermore, we investigate different colour spaces to identify most suitable for this purpose. The feature sets tested are based on a generalization of some existent grey scale approaches for feature extraction to colour images. The generalization has been applied to the calculation of Grey-Level Co-Occurrence Matrix, Grey-Level Difference Matrix and Grey-Level Run-Length Matrix. Furthermore, we calculate Grey-Level Run-Length Matrix starting from the Grey-Level Difference Matrix. The resulting feature sets performances have been compared using the Support Vector Machine model. To validate our method we have used three different databases, HistologyDS, Pap-smear and Lymphoma, that present different medical problems and so they represent different classification problems. The obtained experimental results have showed that in general features extracted from the HSV colour space perform better than the other and that the best feature subset has been obtained from the generalized Grey-Level Co-Occurrence Matrix, demonstrating excellent performances for this purpose.


Medical image analysis Features extraction Machine learning Colour texture classification 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Orlov, N.V., Chen, W., Eckley, D.M., Macura, T., Shamir, L., Jaffe, E.S., Goldberg, I.G.: Automatic Classification of Lymphoma Images with Transform-based Global Features. IEEE Transactions on Information Technology in Biomedicine 14(4), 1003–1013 (2010)CrossRefGoogle Scholar
  2. 2.
    Ameling, S., Wirth, S., Paulus, D., Lacey, G., Vilarino, F.: Texture-based polyp detection in colonoscopy. Bildverarbeitung fr die Medizin, pp. 346–350 (2009)Google Scholar
  3. 3.
    Karkanis, S.A., Iakovidis, D.K., Maroulis, D.E., Karras, D.A., Tzivras, M.: Computer-aided tumor detection in endoscopic video using color wavelet features. IEEE Transaction on Information Technology in Biomedicine 7(3), 141–152 (2003)CrossRefGoogle Scholar
  4. 4.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  5. 5.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  6. 6.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. IEEE Computer Society Conference on Conference on Computer Vision and Pattern Recognition (CVPR) 1, 886–893 (2005)Google Scholar
  7. 7.
    Ojala, T., Pietikinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recognition 29(1), 51–59 (1996)CrossRefGoogle Scholar
  8. 8.
    Jain, A.K., Farrokhnia, F.: Unsupervised texture segmentation using Gabor filters. In: IEEE International Conference on Systems, Man and Cybernetics, pp. 14–19 (1990)Google Scholar
  9. 9.
    Gelzinis, A., Verikas, A., Bacauskiene, M.: Increasing the discrimination power of the co-occurrence matrix-based features. Pattern Recognition 40(9), 2367–2372 (2007)CrossRefzbMATHGoogle Scholar
  10. 10.
    Walker, R., Jackway, P., Longstaff, D.: Genetic algorithm optimization of adaptive multi-scale GLCM features. International Journal of Pattern Recognition and Artificial Intelligence 17(1), 17–39 (2003)CrossRefGoogle Scholar
  11. 11.
    Chen, S., Chengdong, W., Chen, D., Tan, W.: Scene classification based on gray level-gradient co-occurrence matrix in the neighborhood of interest points. IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS), pp. 482–485 (2009)Google Scholar
  12. 12.
    Mitrea, D., Mitrea, P., Nedevschi, S., Badea, R., Lupsor, M.: Abdominal tumor characterization and recognition using superior-order cooccurrence matrices, based on ultrasound images. Computational and Mathematical Methods in Medicine 2012 (2012)Google Scholar
  13. 13.
    Hu, Y.: Unsupervised texture classification by combining multi-scale features and k-means classifier. In: Chinese Conference on Pattern Recognition, pp. 1–5 (2009)Google Scholar
  14. 14.
    Gong, R., Wang, H.: Steganalysis for GIF images based on colors-gradient co-occurrence matrix. Optics Communications 285(24), 4961–4965 (2012)CrossRefGoogle Scholar
  15. 15.
    Nanni, L., Brahnam, S., Ghidoni, S., Menegatti, E., Barrier, T.: Different Approaches for Extracting Information from the Co-Occurrence Matrix. PLoS One 8(12) (2013)Google Scholar
  16. 16.
    Benco, M., Hudec, R.: Novel method for color textures features extraction based on GLCM. Radioengineering 4(16), 64–67 (2007)Google Scholar
  17. 17.
    Putzu, L., Di Ruberto, C.: Investigation of different classification models to determine the presence of leukemia in peripheral blood image. In: Petrosino, A. (ed.) ICIAP 2013, Part I. LNCS, vol. 8156, pp. 612–621. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  18. 18.
    Cruz-Roa, A., Caicedo, J.C., Gonzlez, F.A.: Visual Pattern Mining in Histology Image Collections Using Bag of Features. Artificial Intelligence in Medicine 52(2), 91–106 (2011)CrossRefGoogle Scholar
  19. 19.
    Jantzen, J., Dounias, G.: Analysis of pap-smear data. In: NISIS 2006, Puerto de la Cruz, Tenerife, Spain (2006)Google Scholar
  20. 20.
    Shamir, L., Orlov, N., Eckley, D.M., Macura, T., Goldberg, I.G.: A Proposed Benchmark Suite for Biological Image Analysis. Medical and Biological Engineering and Computing 46(9), 943–947 (2008)CrossRefGoogle Scholar
  21. 21.
    Gonzlez-Rufino, E., Carrin, P., Cernadas, E., Fernndez-Delgado, M., Domnguez-Petit, R.: Exhaustive comparison of colour texture features and classification methods to discriminate cells categories in histological images of fish ovary. Pattern Recognition 46(9), 2391–2407 (2013)CrossRefGoogle Scholar
  22. 22.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural Features for Image Classification. IEEE Transactions on Systems, Man and Cybernetics 3(6), 610–621 (1973)CrossRefGoogle Scholar
  23. 23.
    Conners, R.W., Harlow, C.A.: A Theoretical Comparison of Texture Algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) (3), 204–222 (1980)Google Scholar
  24. 24.
    Tang, X.: Texture Information in Run-Length Matrices. IEEE Transactions Image Processing 7(11), 1602–1609 (1998)CrossRefGoogle Scholar
  25. 25.
    Di Ruberto, C., Fodde, G., Putzu, L.: Comparison of statistical features for medical colour image classification. In: Nalpantidis, L., Krüger, V., Eklundh, J.-O., Gasteratos, A. (eds.) ICVS 2015. LNCS, vol. 9163, pp. 3–13. Springer, Heidelberg (2015) CrossRefGoogle Scholar
  26. 26.
    Porebski, A., Vandenbroucke, N., Hamad, D.: LBP histogram selection for supervised color texture classification. In: IEEE International Conference on Image Processing (ICIP), pp. 3239–3243 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Cecilia Di Ruberto
    • 1
  • Giuseppe Fodde
    • 1
  • Lorenzo Putzu
    • 1
    Email author
  1. 1.Department of Mathematics and Computer ScienceUniversity of CagliariCagliariItaly

Personalised recommendations