QUANTIFICATION OF MELANIN AND HEMOGLOBIN IN HUMAN SKIN FROM MULTISPECTRAL IMAGE ACQUISITION: USE OF A NEURONAL NETWORK COMBINED TO A NON-NEGATIVE MATRIX FACTORIZATION.
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Date publication
janvier 2012
Auteurs
Membres identifiés du Cancéropôle Est :
Pr MARZANI Franck
Tous les auteurs :
Galeano J, Jolivot R, Marzani F
Lien Pubmed
Résumé
This article presents a multispectral imaging system which, coupled with a neural network-based algorithm, reconstructs reflectance cubes. The reflectance spectra are obtained using artificial neural-netwok reconstruction which generates reflectance cubes from acquired multispectral images. Then, a blind source separation algorithm based on Non-negative Matrix Factorization is used for the decomposition of human skin absorption spectra in its main pigments: melanin and hemoglobin. The analysis is performed on reflectance spectra. 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.
Référence
. 2012;11(2):257-70.