Abstract
Diagnosis of benign and malign skin lesions is currently mostly relying on visual assessment and frequent biopsies performed by dermatologists. As the timely and correct diagnosis of these skin lesions is one of the most important factors in the therapeutic outcome, leveraging new technologies to assist the dermatologist seems natural. In this paper we propose a machine learning approach to classify melanocytic lesions into malignant and benign from dermoscopic images. The dermoscopic image database is composed of 4240 benign lesions and 232 malignant melanoma. For segmentation we are using multiphase soft segmentation with total variation and H 1 regularization. Then, each lesion is characterized by a feature vector that contains shape, color and texture information, as well as local and global parameters that try to reflect structures used in medical diagnosis. The learning and classification stage is performed using SVM with polynomial kernels. The classification delivered accuracy of 98.57% with a true positive rate of 0.991% and a false positive rate of 0.019%.
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
Marks, R.: Epidemiology of melanoma. Clin. Exp. Dermatol. 25, 459–463 (2000)
World Health Organization, Ultraviolet Radiation and the Intersun Programme (2007), http://www.who.int/uv/faq/skincancer/en/
Pariser, R.J., Pariser, D.M.: Primary care physicians errors in handling cutaneous disorders. J. Amer. Acad. Dermatol. 17, 239–245 (1987)
Carli, P., De Giorgi, V., Gianotti, B., et al.: Dermatoscopy and early diagnosis of melanoma. Arch Dermotal. 137, 1641–1644 (2001)
Rubegni, P., Burroni, M., Dell’eva, G., Andreassi, L.: Digital dermoscopy analysis for automated diagnosis of pigmented skin lesion. Clinics in Dermatology 20(3), 309–312 (2002)
Nachbar, F., Stolz, W., Merkle, T., Cognetta, A., Vogt, T., Landthaler, M., Bilek, P., Braun-Falco, O., Plewig, G.: The ABCD rule of dermatoscopy: high prospective value in the diagnosis of doubtful melanocytic skin lesions. Journal of the American Academy of Dermatology 30(4), 551–559 (1994)
Lorentzen, H., Weismann, K., Kenet, R., Secher, L., Larsen, F.: Comparison of dermatoscopic abcd rule and risk stratification in the diagnosis of malignant melanoma. Acta Derm Venereol 80(2), 122–126 (2000)
Johr, R.H.: Dermoscopy: alternative melanocytic algorithms - the abcd rule of dermatoscopy, menzies scoring method, and 7–point checklist. Clinics in Dermatology 20(3), 240–247 (2002)
Schmid-Saugeon, P., Guillod, J., Thiran, J.-P.: Towards a Computer–aided diagnosis System for Pigmented Skin Lesions, Comp. Med. Imag. Graphics, pp. 65–78 (2003)
Hall, P.N., Claridge, E., Smith, J.D.: Computer Screening for Early Detection of Melanoma: Is there a Future? British J. Dermatol. 132, 325–328 (1995)
Grzymala-Busse, P., Grzymala-Busse, J.W., Hippe, Z.S.: Melanoma prediction using data mining system LERS. pp. 615–620 (2001)
Cascinelli, N., Ferrario, M., Tonelli, T., Leo, E.: A possible new tool for clinical diagnosis of melanoma: The computer. Journal of the American Academy of Dermatology 16(2), 361–367 (1987)
Ganster, H., Pinz, A., Rhrer, R., Wildling, E., Binder, M., Kittler, H.: Automated melanoma recognition. IEEE Transactions on Medical Imaging vol 20, 233–239 (2001)
Capdehourat, G., Corez, A., Bazzano, A., Muse, P.: Pigmented Skin Lesions Classification Using Dermatoscopic Images (2009) ISBN: 978-3-642-10267-7
Celebi, M.E., Kingravi, H.A., Uddin, B., Iyatomi, H., Aslandogan, Y.A., Stoecker, W.V., Moss, R.H.: A methodological approach to the classification of dermoscopy images. Comput. Med. Imaging Graph 31(6), 362–373 (2007)
Robnik-Sikonja, M., Kononenko, I.: Theoretical and empirical analysis of relieff and rrelieff. Mach. Learn. 53(1-2), 23–69 (2003)
Hall, M.A.: Correlation–based feature selection for discrete and numeric class machine learning, pp. 359–366 (2000)
Schlkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond. The MIT Press, Cambridge (2001)
Li, F., Shen, C., Li, C.: Multiphase Soft Segmentation with Total Variation and H1 Regularization. Journal of Mathematical Imaging and Vision 37(2), 98–111 (2010)
Cristianini, N., Shawe–Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press (2000) ISBN:0521780195
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Safi, A. et al. (2012). Computer–Aided Diagnosis of Pigmented Skin Dermoscopic Images. In: Müller, H., Greenspan, H., Syeda-Mahmood, T. (eds) Medical Content-Based Retrieval for Clinical Decision Support. MCBR-CDS 2011. Lecture Notes in Computer Science, vol 7075. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28460-1_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-28460-1_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28459-5
Online ISBN: 978-3-642-28460-1
eBook Packages: Computer ScienceComputer Science (R0)