Analysis of human skin hyper-spectral images by non-negative matrix factorization

Abstract : 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 coeffi cient 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.
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July A Galeano Z, Romuald Jolivot, Franck Marzani. Analysis of human skin hyper-spectral images by non-negative matrix factorization. 10th Mexican International Conference on Artificial Intelligence, Nov 2011, Puebla, Mexico. pp.431-442. ⟨hal-00790466⟩

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