Multi-spectral image analysis for skin pigmentation classification

Abstract

In this paper, we compare two different approaches for semiautomatic detection of skin hyper-pigmentation on multispectral images. These two methods are support vector machine (SVM) and blind source separation. To apply SVM, a dimension reduction method adapted to data classification is proposed. It allows to improve the quality of SVM classification as well as to have reasonable computation time. For the blind source separation approach we show that, using independent component analysis, it is possible to extract a relevant cartography of skin pigmentation.

Reduction of shading-derived artifacts in skin chromophore imaging without measurements or assumptions about the shape of the subject

“…1,9 Although scattering effects are fully considered in these methods, the methods are susceptible to shading-derived errors caused by the incomplete estimation of the irradiance. 8,11,12 This is a serious problem for visual understanding and quantitative evaluation of the chromophore imaging.…”

“…Neither the nonlinearity between the concentration and the absorbance nor the cross-talk between the chromophores has been taken into account. 1,14,15 In order to fully account for the effect of scattering in the estimation of chromophore concentrations, several methods have been considered; these include methods based on random work theory, 12 Kubelka-Munk theory, 8 a support vector machine, 11 and an empirical method aided by a Monte Carlo simulation (MCS). 1,9 Although scattering effects are fully considered in these methods, the methods are susceptible to shading-derived errors caused by the incomplete estimation of the irradiance.…”

“…Therefore, many studies have been conducted to evaluate the concentration of chromophores, [1][2][3][4][5][6] and some of these studies discuss imaging, in which visible light spectroscopy is usually used to achieve high spatial resolution. [7][8][9][10][11][12][13] Multiple regression analysis (MRA) based on the Beer-Lambert (B-L) 3 law is a simple way to correlate the chromophore concentrations with the reflectance of skin. The B-L law has been applied to the three-layer skin model of epidermis, dermis, and fat, with the assumptions that no scattering occurs in the epidermis or dermis and no wavelength dependency exists in the reflectance from the fat layer.…”

Estimation of an optimal spectral band combination to evaluate skin disease treatment efficacy using multi-spectral images

“…For the same reason as above, the chosen normalization is the one of equation (5). Then the final measurement of the severity according to the studied treatment A compared to the reference treatment V is:…”

“…To introduce the spectral information, an algorithm proposed in [3] performs a source separation of the melanin and haemoglobin components from an empirical analysis of their absorption. Methods based on image processing techniques have been proposed in [4,5] to perform a classification of healthy/pathological areas with multispectral images. Once the healthy and the pathological areas are classified, a criterion has to be defined.…”