Abstract
Malignant melanoma is the most deadly form of skin lesion. Early diagnosis is of critical importance to patient survival. Existent visual recognition algorithms for skin lesions classification focus mostly on segmentation and feature extraction. In this paper instead we put the emphasis on the learning process by using two kernel-based classifiers. We chose a discriminative approach using support vector machines, and a probabilistic approach using spin glass-Markov random fields. We benchmarked these algorithms against the (to our knowledge) state-of-the-art method on melanoma recognition, exploring how performance changes by using color or textural features, and how it is affected by the quality of the segmentation mask. We show with extensive experiments that the support vector machine approach outperforms the existing method and, on two classes out of three, it achieves performances comparable to those obtained by expert clinicians.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Amit, D.J.: Modeling Brain Function. Cambridge University Press, Cambridge, USA (1989)
Caputo, B.: A new kernel method for object recognition: spin glass Markov random fields. PhD thesis, Stockholm (November 2004), Available at: http://www.nada.kth.se/~caputo
Caputo, B., La Torre, E., Bouattour, S., Gigante, G.E.: A New Kernel Method for Microcalcification Detection: Spin Glass- Markov Random Fields. In: Proc. of MIE 2002, Budapest (August 2002)
De Vries, E., Bray, F.I., Coebergh, J.W.W., Parkin, D.M.: Changing Epidemiology of Malignant Cutaneous Melanoma in Europe 1953-1997: Rising Trends in Incidence and Mortality but Recent Stabilizations in Western Europe and Decreases in Scandinavia. Int. J. Cancer 107, 119–126 (2003)
Ganster, H., Pinz, A., Rhrer, R., Wildling, E., Binder, M., Kittler, H.: Automated Melanoma Recognition. IEEE Trans on MI 20(3) (March 2001)
Grana, C., Pellacani, G., Cucchiara, R., Seidenari, S.: A New Algorithm for Border Description of Polarized Light Surface Microscopic Images of Pigmented Skin Lesions. IEEE Trans on MI 22(8) (August 2003)
Grzymala-Busse, J.P., Grzymala-Busse, J.W., Hippe, Z.S.: Melanoma Prediction Using Data Mining System LERS. In: Proc COMPSAC 2001, pp. 615–620 (2001)
Lefevre, E., Colot, O., Vannoorenberghe, P., de Brucq, D.: Knowledge modeling methods in the framework of Evidence Theory An experimental comparison for melanoma detection. In: Proc. of Int. Conf. on Systems, Man, and Cybernetics, vol. 4, pp. 2806–2811
Rigel, D.S., Carucci, J.A.: Malignant Melanoma: Prevention, Early Detection, and Treatment in the 21st Century. CA Cancer J. Clin. 50, 215–236 (2000)
Schiele, B., Crowley, J.L.: Recognition without correspondence using Multidimensional Receptive Field Hisograms. IJCV 36(1), 31–52 (2000)
Scholkopf, B., Smola, A.J.: Learning with kernels. MIT Press, Cambridge (2001)
Vapnik, V.: Statistical learning theory. Wiley and Son, Chichester (1998)
Wallraven, C., Caputo, B., Graf, A.: Recognition with Local features: the kernel recipe. In: Proc. ICCV 2003 (2003)
Wei, L., Yang, Y., Nishikawa, R.M., Jiang, Y.: A Study on Several Machine-Learning Methods for Classification od Malignant and Benign Clustered Microcalcifications. IEEE Trans. On MI 24(3) (March 2005)
Informations available at the World Healt Organization website: http://www.who.int
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tommasi, T., La Torre, E., Caputo, B. (2006). Melanoma Recognition Using Representative and Discriminative Kernel Classifiers. In: Beichel, R.R., Sonka, M. (eds) Computer Vision Approaches to Medical Image Analysis. CVAMIA 2006. Lecture Notes in Computer Science, vol 4241. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11889762_1
Download citation
DOI: https://doi.org/10.1007/11889762_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46257-6
Online ISBN: 978-3-540-46258-3
eBook Packages: Computer ScienceComputer Science (R0)