Brain Tumor Segmentation Using Support Vector Machines

  • Raouia Ayachi
  • Nahla Ben Amor
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5590)


One of the challenging tasks in the medical area is brain tumor segmentation which consists on the extraction process of tumor regions from images. Generally, this task is done manually by medical experts which is not always obvious due to the similarity between tumor and normal tissues and the high diversity in tumors appearance. Thus, automating medical image segmentation remains a real challenge which has attracted the attention of several researchers in last years. In this paper, we will focus on segmentation of Magnetic Resonance brain Images (MRI). Our idea is to consider this problem as a classification problem where the aim is to distinguish between normal and abnormal pixels on the basis of several features, namely intensities and texture. More precisely, we propose to use Support Vector Machine (SVM) which is within popular and well motivating classification methods. The experimental study will be carried on Gliomas dataset representing different tumor shapes, locations, sizes and image intensities.


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  1. 1.
    Atkins, M.S., Mackiewich, B.T.: Fully automatic segmentation of the brain in MRI. IEEE Transactions on Medical Imaging 17(1), 98–107 (1998)CrossRefGoogle Scholar
  2. 2.
    Lecoeur, J., Barillot, C.: Segmentation d’images cérébrales: Etat de l’art. Rapport de Recherche INRIA 6306 (September 2007)Google Scholar
  3. 3.
    Jiang, C., Zhang, X., Huang, W., Meinel, C.: Segmentation and Quantification of Brain Tumor. In: IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems, pp. 12–14 (2004)Google Scholar
  4. 4.
    Prastawa, M., Bullitt, E., Moon, N.: Automatic brain tumor segmentation by subject specific modification of atlas priors. Acad. Radiol. 10(12), 1341–1348 (2003)CrossRefGoogle Scholar
  5. 5.
    Schmidt, M.: Automatic brain tumor segmentation. University of Alberta, Department of computing science (2005)Google Scholar
  6. 6.
    Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(7), 629–639 (1990)CrossRefGoogle Scholar
  7. 7.
    Dickson, S., Thomas, B.: Using neural networks to automatically detect brain tumours in MR images. International Journal of Neural Systems 4(1), 91–99 (1997)CrossRefGoogle Scholar
  8. 8.
    Kaus, M., Warfield, S., Nabavi, A., Black, P., Jolesz, F., Kikinis, R.: Automated segmentation of MR images of brain tumors. Radiology 218(2), 586–591 (2001)CrossRefGoogle Scholar
  9. 9.
    Prastawa, M., Bullitt, E., Ho, S., Gerig, G.: Brain tumor segmentation framework based on outlier detection. Medical Image Analysis 8(3), 275–283 (2004)CrossRefGoogle Scholar
  10. 10.
    Gunn, S.R.: Support vector machine for classification and regression. Technical report Faculty of Engineering, Science and Mathematics School of Electronics and Computer Science (1998)Google Scholar
  11. 11.
    Materka, A., Strzelecki, M.: Texture analysis methods: a review. Technical report COST B11 Technical University of Lodz Poland (1998)Google Scholar
  12. 12.
    Clark, M.C., Hall, L.O., Goldgof, D.B., Velthuizen, R., Murtagh, F.R., Silbiger, M.S.: Automatic tumor segmentation using knowledge based techniques. IEEE Trans. on Medical Imaging 17(2), 238–251 (1998)CrossRefGoogle Scholar
  13. 13.
    Forsyth, D., Ponce, J.: Computer Vision: A Modern Approach. Prentice-Hall, Englewood Cliffs (2002)Google Scholar
  14. 14.
    Hayman, E., Caputo, E., Fritz, M., Eklundh, J.: On the significance of real-world conditions for material classification. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 253–266. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  15. 15.
    Haralick, R., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. on Systems Man and Cybern 3(6), 610–621 (1973)CrossRefGoogle Scholar
  16. 16.
    Boser, B.E., Guyon, I., Vapnik, V.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 144–152. ACM Press, New York (1992)Google Scholar
  17. 17.
    Stoitsis, J., et al.: Computer aided diagnosis based on medical image processing and artificial intelligence methods. Nuclear Instruments and Methods in Physics Research 569(2), 591–595 (2006)CrossRefGoogle Scholar
  18. 18.
    Kass, M., et al.: Snakes: Active Contour Models. International Journal of Computer Vision 1(4), 321–331 (1988)CrossRefGoogle Scholar
  19. 19.
    Evans, A., et al.: An mri-based stereotactic atlas from 250 young normal subjects. Society for Neuroscience Abstracts 18, 408 (1992)Google Scholar
  20. 20.
    Scholkopf, B., Smola, A.J.: Learning with Kernels Support Vector Machines, Regularization, Optimization and Beyond. MIT Press, Cambridge (2001)Google Scholar
  21. 21.
    Chan, K., Lee, T.W., Sample, P.A., Goldbaum, M., Weinreb, R.N.: Comparison of machine learning and traditional classifers in glaucoma diagnosis. IEEE Trans. on Biomedical Engineering 49(9), 963–974 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Raouia Ayachi
    • 1
  • Nahla Ben Amor
    • 1
  1. 1.LARODEC, Institut Supérieur de Gestion TunisLe BardoTunisie

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