Human skin has been studied in numerous investigations, given the interest in knowing information about physiology, morphology and chemical composition. These parameters can be determined using non invasively optical techniques in vivo, such as the diffuse reflectance spectroscopy. The human skin color is determined by many factors, but primarily by the amount and distribution of the pigment melanin. The melanin is produced by the melanocytes in the basal layer of the epidermis. This research characterize the spectral response of the human skin using the coefficients of Fourier series expansion. Simulating the radiative transfer equation for the Monte Carlo method to vary the concentration of the melanocytes (fme) in a simplified model of human skin. It fits relating the Fourier series coefficient a0 with fme. Therefore it is possible to recover the skin biophysical parameter.
The determination of the optical parameters are important for remote sensing and aircraft, in this case allow the difference between a cloud composed solely of water and water plus ash. Therefore, this research is intended to determine the optical properties of the ash four active volcanoes, by studying the spectral resolution reflectance interpreting the results in the approximation of Kubelka - Munk equation through the transfer equation radiative. The results allow classifying these ashes depending on their place of origin.
The noninvasive optical techniques have attracted considerable interest in recent years, because these techniques provide lot of information on the structure and composition of biological tissues more quickly and painlessly, in this study classifies the degrees of histological differentiation of neoplastic tissue of the breast in white adipose tissue samples through numerical pametrización of the diffuse reflection spectra using the Fourier series approximation. The white adipose tissue is irradiated with the spectrophotometer MiniScan XEplus and it from a mastectomy of patients with aged 38 and 50 who have a cancer lesion in the breast. The samples were provided by the pathologist with theirs medical report, it which we indicate the histological grade of tumor. We performed a parameterization algorithm where the classification criterion is the modulus of the minimum difference between the numerical approximation coefficients ai and average numerical approximation coefficients obtained for each histological grade ̄ al. Is confirmed that the cubic spline interpolation this low-power computing lets classified into histological grades with 91% certainty the tissues under study from |ai − ̄ al|