Detection of Tumor Tissue Based on the Multispectral Imaging

  • Adam Świtoński
  • Marcin Michalak
  • Henryk Josiński
  • Konrad Wojciechowski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6375)


We have prepared multispectral image database of skin tumor diagnosis. All images have been labeled with two classes - tumor and healthy tissues. We have extracted pixel signatures with their spectral data and class assigning, thus obtained train dataset. Next we have used and evaluated the supervised learning techniques for the purpose of automatic tumor detection. We have tested Naive Bayes, KNN, Multilayer Perceptron, LibSVM, LibLinear, RBFNetwork, ConjuctiveRule, DecisionTable and PART classifiers. We have obtained results on the level of 99% classifier efficiency. We have visualized classification for example images by coloring class regions and verified if they overlap with labeled regions.


Radial Basis Function Hyperspectral Image Radial Basis Function Neural Network Multilayer Perceptron Markov Chain Model 
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.
    Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the fifth annual workshop on Computational learning theory, pp. 144–152 (1992)Google Scholar
  2. 2.
    Camps-Valls, G., Bruzzone, L.: Kernel-based methods for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 43(6) (2005)Google Scholar
  3. 3.
    Cohen, W.W.: Fast effective rule induction. In: Proceedings of the 12th International Conference on Machine Learning, pp. 115–123Google Scholar
  4. 4.
    Derrode, S., Mercier, G., Pieczynski, W.: Unsupervised multicomponent image segmentation combining a vectorial HMC model and ICA. In: Proceedings of the IEEE International Conference on Image Processing (2003)Google Scholar
  5. 5.
    Drucker, H., Burges, C.J.C., Kaufman, L., Smola, A.J., Vapnik, V.N.: Support vector regression machines. Adv. in Neural Inf. Process. Syst. IX, 155–161 (1997)Google Scholar
  6. 6.
    Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., Lin, C.-J.: LIBLINEAR: A library for large linear classification. Journal of Machine Learning Research 9, 1871–1874 (2008)Google Scholar
  7. 7.
    Frank, E., Witten, I.H.: Generating Accurate Rule Sets Without Global Optimization. In: Fifteenth International Conference on Machine Learning, pp. 144–151 (1998)Google Scholar
  8. 8.
    Gat, N.: Imaging spectroscopy using tunable filters: a review. In: Proc SPIE-Int. Soc. Opt. Eng. (2000)Google Scholar
  9. 9.
    Kohavi, R.: The Power of Decision Tables. In: Lavrač, N., Wrobel, S. (eds.) ECML 1995. LNCS, vol. 912, pp. 174–189. Springer, Heidelberg (1995)Google Scholar
  10. 10.
    Mercier, G., Derrode, S., Lennon, M.: Hyperspectral Image Segmentation with Markov Chain Model. In: IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2003 (2003)Google Scholar
  11. 11.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)Google Scholar
  12. 12.
    Soille, P.: Morphological Image Analysis: Principles and Applications. Springer, Heidelberg (2003)zbMATHGoogle Scholar
  13. 13.
    Witten, I.H., Frank, E.: Data Mining. In: Practical Machine Learning Tools and Techniques, 2nd edn. Elsevier, Amsterdam (2005)Google Scholar
  14. 14.
    Woolfe, F., Maggioni, M., Davis, G., Warner, F., Coifman, R., Zucker, S.: Hyper-spectral microscopic discrimination between normal and cancerous colon biopsies. IEEE Transactions on Medical Imaging 99(99) (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Adam Świtoński
    • 1
    • 2
  • Marcin Michalak
    • 2
    • 3
  • Henryk Josiński
    • 1
    • 2
  • Konrad Wojciechowski
    • 1
    • 2
  1. 1.Polish-Japanese Institute of Information TechnologyBytomPoland
  2. 2.Silesian University of TechnologyGliwicePoland
  3. 3.Central Mining InstituteKatowicePoland

Personalised recommendations