Diabetes is a widespread and serious disease and noninvasive measurement has been in high demand. To address this problem, a power spectral density-based method was offered for determining glucose sensitive sub-bands in the nearinfrared (NIR) spectrum. The experiments were conducted using phantoms of different optical properties in-vitro conditions. The optical bands 1200–1300 nm and 2100–2200 nm were found feasible for measuring blood glucose. After that, a photoplethysmography (PPG)-based low cost and portable optical system was designed. It has six different NIR wavelength LEDs for illumination and an InGaAs photodiode for detection. Optical density values were calculated through the system and used as independent variables for multiple linear regression analysis. The results of blood glucose levels for 24 known healthy subjects showed that the optical system prediction was nearly 80% in the A zone and 20% in the B zone according to the Clarke Error Grid analysis. It was shown that a promising easyuse, continuous, and compact optical system had been designed.

Subcutaneous vein network plays important roles to maintain microcirculation that is related to some diagnostic aspects. Despite developments of optical imaging technologies, still the di±culties about deep skin vascular imaging have been continued. On the other hand, since hemoglobin concentration of human blood has key role in the veins imaging by optical manner, the used wavelength in vascular imaging, must be chosen considering absorption of hemoglobin. In this research, we constructed a near infrared (NIR) light source because of lower absorption of hemoglobin in this optical region. To obtain vascular image, reflectance geometry was used. Next, from recorded images, vascular network analysis, such as calculation of width of vascular of interest and complexity of selected region were implemented. By comparing with other modalities, we observed that proposed imaging system has great advantages including nonionized radiation, moderate penetration depth of 0.5–3mm and diameter of 1mm, cost-effective and algorithmic simplicity for analysis.

Optical parameters (properties) of tissue-mimicking phantoms are determined through nonin-vasive optical imaging. Objective of this study is to decompose obtained diffuse reflectance into these optical properties such as absorption and scattering coefficients. To do so, transmission spectroscopy is firstly used to measure the coefficients via an experimental setup. Next, the optical properties of each characterized phantom are input for Monte Carlo (MC) simulations to get diffuse reflectance. Also, a surface image for each single phantom with its known optical properties is obliquely captured due to reflectance-based geometrical setup using CMOS camera that is positioned at 50 angle to the phantoms. For the illumination of light, a laser light source at 633 nm wavelength is preferred, because optical properties of different components in a biological tissue on that wavelength are nonoverlapped. During in vitro measurements, we prepared 30 different mixture samples adding clinoleic intravenous lipid emulsion (CILE) and evans blue (EB) dye into a distilled water. Finally, all obtained diffuse reflectance values are used to estimate theoptical coefficients by artificial neural networks (ANNs) in inverse modeling. For a biological tissue it is found that the simulated and measured values in our results are in good agreement.