Skin Parameter Map Retrieval from a Dedicated Multispectral Imaging System Applied to Dermatology/Cosmetology

Abstract

In vivo quantitative assessment of skin lesions is an important step in the evaluation of skin condition. An objective measurement device can help as a valuable tool for skin analysis. We propose an explorative new multispectral camera specifically developed for dermatology/cosmetology applications. The multispectral imaging system provides images of skin reflectance at different wavebands covering visible and near-infrared domain. It is coupled with a neural network-based algorithm for the reconstruction of reflectance cube of cutaneous data. This cube contains only skin optical reflectance spectrum in each pixel of the bidimensional spatial information. The reflectance cube is analyzed by an algorithm based on a Kubelka-Munk model combined with evolutionary algorithm. The technique allows quantitative measure of cutaneous tissue and retrieves five skin parameter maps: melanin concentration, epidermis/dermis thickness, haemoglobin concentration, and the oxygenated hemoglobin. The results retrieved on healthy participants by the algorithm are in good accordance with the data from the literature. The usefulness of the developed technique was proved during two experiments: a clinical study based on vitiligo and melasma skin lesions and a skin oxygenation experiment (induced ischemia) with healthy participant where normal tissues are recorded at normal state and when temporary ischemia is induced.

Cutaneous Multispectral Optical tomography With ImageJ

“…Es de importancia hacer notar, que en el proceso de construcción de la imagen en tres dimensiones, se debe tomar en cuenta la radiancia de cada banda ya que ellopermite conocer la profundidad de la lesión. En la piel la absorción está determinada por la presencia de Melanina y Hemoglobina, [9] siendo estas las que determinan la profundidad de penetración de la luz (Figura 2). Al seleccionar la banda adecuada que contenga las longitudes de onda próximas al máximo del espectro de absorción de estos pigmentos, se mejora el contraste de la imagen de la lesión [7].…”

Blind source separation of ex-vivo aorta tissue multispectral images

“…The phenomena of light scattering, absorption and diffuse reflectance are produced in biological tissue by the iteration of light with the tissue biochemical components [11,12,13].…”

Model-based Skin Pigment Cartography by High-Resolution Hyperspectral Imaging

“…Skin chromophores and their influence on skin color perception are a major concern in dermatology and cosmetics in order to evaluate pigmentation issues [1][2][3]. Non-invasive imaging systems of the skin are used in a wide range of clinical applications in the last decades, in particular in order to get information on pigmentation disorders: melanin index, port wine vein, vitiligo, erythema [4][5]… Since pigmentation determines the spectral reflectance of the skin, the best way to observe it is to make images in different spectral bands, either in three wide spectral bands as in classical RGB imaging [6][7], or in more than 10 short spectral bands as in multispectral imaging [8][9].…”

“…The Kubelka-Munk model predicts the reflectance and transmittance of one homogeneous layer as a function of its thickness [18], and the Kubelka compositional model can predict the reflectance and transmittance of stacks of homogenous layers [19]. Two-flux models have been used in a wide range of applications, included skin analysis [3,[20][21][22], most of the time without Saunderson correction [23], which means that the optical effect of the air-skin interface is not taken into account. More advanced models describe more thinly the scattering of light by the skin tissues, especially the angular distribution of the scattered light, thanks to methods based on the radiative transfer theory with a multilayer model [16,[24][25].…”

“…The inversed model, fastened by an optimized algorithm, enables computing rather fast five skin parameters: epidermis depth, melanin volume fraction, blood volume fraction, oxygen saturation and bilirubin volume fraction. These parameters can be displayed under the form of density maps [3]. The estimation error between simulated and real spectrum is computed using least square and spectral angle similarity (SAS) [27].…”