Abstract
In this paper, the methodology of generating images of melanocytic skin lesions is briefly outlined. The developed methodology proceeds essentially in two steps. In the first one, semantic description of skin lesions of anonymous patients is carefully analyzed to catch important features (symptoms) and to mine their logical values. Then, data gained in this step are used to control a specific simulation process, in which the simulated lesion’s image is randomly put together from a priori pre-defined fragments (textures). In this way, a single textual vector representing a distinct lesion, can produce a collection of several images of a given category. The quality of simulated images, verified by an independent expert was found to be quite satisfactory.
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Hippe, Z.S., Grzymała-Busse, J.W., Piątek, Ł. (2006). Randomized Dynamic Generation of Selected Melanocytic Skin Lesion Features. In: Kłopotek, M.A., Wierzchoń, S.T., Trojanowski, K. (eds) Intelligent Information Processing and Web Mining. Advances in Soft Computing, vol 35. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-33521-8_3
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DOI: https://doi.org/10.1007/3-540-33521-8_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33520-7
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