Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
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)
Camps-Valls, G., Bruzzone, L.: Kernel-based methods for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 43(6) (2005)
Cohen, W.W.: Fast effective rule induction. In: Proceedings of the 12th International Conference on Machine Learning, pp. 115–123
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)
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)
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)
Frank, E., Witten, I.H.: Generating Accurate Rule Sets Without Global Optimization. In: Fifteenth International Conference on Machine Learning, pp. 144–151 (1998)
Gat, N.: Imaging spectroscopy using tunable filters: a review. In: Proc SPIE-Int. Soc. Opt. Eng. (2000)
Kohavi, R.: The Power of Decision Tables. In: Lavrač, N., Wrobel, S. (eds.) ECML 1995. LNCS, vol. 912, pp. 174–189. Springer, Heidelberg (1995)
Mercier, G., Derrode, S., Lennon, M.: Hyperspectral Image Segmentation with Markov Chain Model. In: IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2003 (2003)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
Soille, P.: Morphological Image Analysis: Principles and Applications. Springer, Heidelberg (2003)
Witten, I.H., Frank, E.: Data Mining. In: Practical Machine Learning Tools and Techniques, 2nd edn. Elsevier, Amsterdam (2005)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Świtoński, A., Michalak, M., Josiński, H., Wojciechowski, K. (2010). Detection of Tumor Tissue Based on the Multispectral Imaging. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2010. Lecture Notes in Computer Science, vol 6375. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15907-7_40
Download citation
DOI: https://doi.org/10.1007/978-3-642-15907-7_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15906-0
Online ISBN: 978-3-642-15907-7
eBook Packages: Computer ScienceComputer Science (R0)