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

Abstract

The determination of local components in human skin from in-vivo spectral reflectance measurements is crucial for medical applications, especially for aiding the diagnostic of skin diseases. Hyper-spectral imaging is a convenient technique since one spectrum is acquired in each pixel of the image, and by inverting a light scattering model, we can retrieve the concentrations of skin components in each pixel. The good performance of the method presented in this paper comes from both the imaging system and the model. The hyper-spectral camera that we conceived uses polarizing filters in order to remove gloss effects generated by the stratum corneum; it provides a high resolution image (1120 × 900 pixels), with a thin spectral sampling of 10 nm over the visible spectrum. The acquisition time of 2 seconds is short enough to prevent movement effects of the imaged area, which is usually the main issue in hyperspectral imaging. The model relies on a two-layer model for the skin, and the Kubelka-Munk theory with Saunderson correction for the light reflection. An optimization method enables computing, in less than one hour, several skin parameters in each of the million of pixels. These parameters (blood, melanin and bilirubin volume fractions, oxygen saturation…) are then displayed under the form of density images. Different skin structures, such as veins, blood capillaries, hematoma or pigmented spots, can be highlighted. The deviation between the measured spectrum and the one computed from the fitted parameters is evaluated in each pixel.

<p>Identification Of Three Key Factors Contributing To The Aetiology Of Dark Circles By Clinical And Instrumental Assessments Of The Infraorbital Region</p>

“…This technology allows the user to select specific wavelength to assess the presence of melanin and haemoglobin, as well as performing nCDA of the overall dark circle area. 17 Some published articles have already highlighted the benefit of this technique to assess skin colour, 18 and even more specifically, skin pigment, 19 erythema, 20 melanin, total haemoglobin and oxygen saturation in dark circles. 21 The multispectral analysis performed in this research showed a higher melanin index that suggests hyperpigmentation, and a higher haemoglobin index that supports the hypothesis of blood vessel dilation in the dark circles area.…”

SpectraCam®: A new polarized hyperspectral imaging system for repeatable and reproducible in vivo skin quantification of melanin, total hemoglobin, and oxygen saturation

“…SpectraCam ® (Newtone Technologies, Lyon, France) is a portable camera that collects in vivo reflectance data from human skin ( Figure 1 21 Light emitted by LEDs is transmitted through the polarizers (P) and reflected by the skin. A tunable filter (TF) let pass a specific wavelength and is used as analyzer.…”

Three-dimensional hyperspectral imaging: a new method for human face acquisition

“…However, as the computation time needed to analyze the spectral information increases proportionally to the number of pixels, the skin model and the optical model chosen should be as simple as possible, to make the time needed for data analysis acceptable. In a previous work [5], a Kubelka-Munk inspired model applied onto a two-layer skin structure was shown to give satisfactory results by computing up to five skin composition maps of 1120 × 900 pixels in less than one hour. The hyperspectral images were captured by using the SpectraCam® camera [5] [10] developed by Newtone Technologies.…”

“…In recent years, research in the measurement of skin optical properties has focused on non-invasive in vivo methods as ex vivo methods are known to alter the properties of the skin, and as invasive analysis may cause patient discomfort. Several optical methods have emerged, such as diffuse reflectance spectroscopy [2], modulated imaging [3], spectral imaging, and color imaging [4] [5]. Data retrieved from these methods have been analyzed using various models such as the Monte Carlo method [6], the radiative transfer equation [7], the diffusion approximation [3], flux models such as the Kubelka Munk model [5], and independent component analysis [8].…”