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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Analysis of Human Skin Hyper-Spectral Images by Non-negative Matrix Factorization
verfasst von : July Galeano, Romuald Jolivot, Franck Marzani
Erschienen in: Advances in Soft Computing
Verlag: Springer Berlin Heidelberg
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This article presents the use of Non-negative Matrix Factorization, a blind source separation algorithm, for the decomposition of human skin absorption spectra in its main pigments: melanin and hemoglobin. The evaluated spectra come from a Hyper-Spectral Image, which is the result of the processing of a Multi-Spectral Image by a neural network-based algorithm. The implemented source separation algorithm is based on a multiplicative coefficient upload. The goal is to represent a given spectrum as the weighted sum of two spectral components. The resulting weighted coefficients are used to quantify melanin and hemoglobin content in the given spectra. Results present a degree of correlation higher than 90% compared to theoretical hemoglobin and melanin spectra. This methodology is validated on 35 melasma lesions from a population of 10 subjects.