@conference{10902/13291,
year = {2017},
url = {http://hdl.handle.net/10902/13291},
abstract = {Skin lesion segmentation is a complex step for dermoscopy pathological diagnosis. Kernel density estimation is proposed as a segmentation technique based on the statistic distribution of color intensities in the lesion and non-lesion regions.},
organization = {This work is supported by the “Ministerio de Economía, Industria y Competitividad” (MINECO) under projects DA2TOI (FIS2010-19860), SENSA (TEC2016-76021-C2-2-R), the “Instituto de Salud Carlos III” (ISCIII) through projects FUSIODERM (DTS15/00238) and CIBERBBN and the co-financed by FEDER funds.},
publisher = {SPIE Society of Photo-Optical Instrumentation Engineers-},
publisher = {The Optical Society (OSA)},
publisher = {A. Pardo, E. Real, G. Fernandez-Barreras, F. Madruga, J. López-Higuera, and O. Conde, "Automated skin lesion segmentation with kernel density estimation," in European Conference on Biomedical Optics: Clinical and Preclinical Optical Diagnostics, J. Quincy Brown and Ton G. van Leeuwen,  eds., Vol. 10411 of Proceedings of SPIE-OSA Biomedical Optics, 104110P, (2017)},
title = {Automated skin lesion segmentation with kernel density estimation},
author = {Pardo Franco, Arturo and Real Peña, Eusebio and Fernández Barreras, Gaspar and Madruga Saavedra, Francisco Javier and López Higuera, José Miguel and Conde Portilla, Olga María},
}