Summary
The paper presents a verified project of a computer system for multistrategic classification of melanocytic skin lesions, based on image analysis reinforced by machine learning and decision-making algorithms with the use of voting procedures. We applied Stolz, Menzies and Argenziano strategies.
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
http://www.dermoncology.med.nyu.edu/MMfacts.htm (April 28, 2001)
Triller, R., Aitken, G., Luc, T.: Atlas of Dermatoscopy of Pigmented Skin Tumors (2008), http://www.plessfr/dermatoscopie
Stolz, W., Braun-Falco, O., Bilek, P., Landthaler, M., Burgdorf, W., Cognetta, A.: Atlas of Dermatoscopy. In: Czelej (ed.) Office, Lublin, Poland, p. 210 (2006) (in Polish)
Menzies, S.W.: Surface microscopy of pigmented skin tumors. Australas J. Dermatol. 38, 40–43 (1997)
Argenziano, G., Fabbrocini, G., Carli, P., De Giorgi, V., Sammarco, E., Delfino, M.: Epiluminescence microscopy for the diagnosis of doubtful melanocytic skin lesions. Comparison of the ABCD rule of dermatoscopy and a new 7-point checklist based on pattern analysis. Arch. Dermatol. 134, 1563–1570 (1998)
Cichosz, P.: Learning Systems, pp. 565–567. WNT, Warsaw (2000) (in Polish)
Grzymala-Busse, J.W., Hippe, Z.S., Knap, M., Paja, W.: Infoscience Technology: The Impact of Internet Accessible Melanoid Data on Health Issues. Data Science Journal 4, 77–81 (2005)
Triller, R.: Quiz of Dermatoscopy. Diagnosis of pigmented skin tumors (2008)
Ludwiczuk, R.: Canny Edge Detection Algorithm in Medical Image Segmentation Process. In: II Conference of Computer Enthusiasts, Cheşm (2003) (in Polish)
Czechowicz, A., Mikrut, S.: Review of Edge Detection Algorithms in Close Range Photogrammetry. Archive of Photogrammetry, Cartography and Teledetection 16, 135–146 (2006) (in Polish)
Ng, V., Cheung, D.: Measuring Asymmetry of Skin Lesions. Proc. Computational Cybernetics and Simulationapos, 4211–4216 (1997)
d’Amico, M., Ferri, M., Stanganelli, I.: Qualitative Asymmetry Measure for melanoma detection. In: Proc. International Symposium on Volume, pp. 1155–1158 (2004)
Seidenari, S., Burroni, M., Dell’Eva, G., Pepe, P.: Computerized evaluation of pigmented skin lesion images recorded by a videomicroscope: compersion between polarizing mode observation and oil/slide mode observation. Skin Res. Technol., 187–191 (1995)
Green, A., Martin, N., Pfitzner, J., O’Rourke, M.: Computer image analysis in the diagnosis of melanoma. Journal of the American Academy of Dermatology 31, 958–964 (1994)
Horsch, A., Stolz, W., Neiss, A., Abmayr, W., Pompl, R., Bernklau, A., Bunk, W., Dersch, D.R., Glassl, A., Achiffner, R., Morfill, G.: Improving early recognition of malignant melanomas by digital image analysis in dermatoscopy. Stud. Health Technol. Inform. 43, 531–535 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Cudek, P., Grzymala-Busse, J.W., Hippe, Z.S. (2009). Multistrategic Classification System of Melanocytic Skin Lesions: Architecture and First Results. In: Kurzynski, M., Wozniak, M. (eds) Computer Recognition Systems 3. Advances in Intelligent and Soft Computing, vol 57. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-93905-4_45
Download citation
DOI: https://doi.org/10.1007/978-3-540-93905-4_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-93904-7
Online ISBN: 978-3-540-93905-4
eBook Packages: EngineeringEngineering (R0)
Keywords
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.