Quantification of melanin and hemoglobin in humain skin from multispectral image acquisition: use of a neuronal network combined to a non-negative matrix factorization

Abstract : 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.
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Article dans une revue
Applied and Computational Mathematics, special issue on Applied Artificial Intelligence and Soft Computing, 2012, 11 (2), pp.257-270
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July A Galeano Z, Romuald Jolivot, Franck Marzani. Quantification of melanin and hemoglobin in humain skin from multispectral image acquisition: use of a neuronal network combined to a non-negative matrix factorization. Applied and Computational Mathematics, special issue on Applied Artificial Intelligence and Soft Computing, 2012, 11 (2), pp.257-270. <hal-00790445>

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