Dermoscopy is a non-invasive diagnostic technique for the in vivo observation of pigmented skin lesions used in dermatology. There is currently a great interest in the prospects of automatic image analysis methods for dermoscopy, both to provide quantitative information about a lesion, which can be of relevance for the clinician, and as a stand alone early warning tool. The standard approach in automatic dermoscopic image analysis has usually three stages: (i) segmentation, (ii) feature extraction and selection, (iii) lesion classification. This paper evaluates the potential of an alternative approach based on the Menzies method - presence of 1 or more of 6 color classes, indicating that the lesion should be considered a potential melanoma. This method does not require stages (i) and (ii) - lesion segmentation and feature extraction. The Jeffries-Matusita and Transformed Divergence metrics were used to evaluate the color class separability. The preliminary results presented in this paper suggest that a system based on the Menzies method could provide valuable information for automatic dermoscopic image analysis. © 2012 Springer-Verlag.
CITATION STYLE
Silva, C. S. P., Marcal, A. R. S., Pereira, M. A., Mendonça, T., & Rozeira, J. (2012). Separability analysis of color classes on dermoscopic images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7325 LNCS, pp. 268–277). https://doi.org/10.1007/978-3-642-31298-4_32
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