In tissue optics, it is important to measure the wavelength-dependent scattering, absorption and anisotropy coefficients of tissues to describe interactions of light with such turbid media. Here, we use the inverse adding-doubling (IAD) technique coupled to measurements acquired using an integrating sphere (IS). The IS system provides a method to acquire highly accurate measurements for the total reflectance and transmittance for thin turbid samples. The IAD is an iterative technique that uses a numerical solver to radiative transport capable of fitting a set of measured reflectance and transmittance values and thereby yield optical absorption and reduced scattering coefficients of thin samples. We test the validity and performance of the IS/IAD system by obtaining measurements on a set of liquid phantoms prepared with controlled absorption and scattering properties. We explore sources of errors and discuss how the the accuracy these techniques may be improved. We demonstrate that the IAD/IS technique allows the accurate recovery of chromophore spectral properties.
Video Photoplethysmography (VPPG) is a numerical technique to process standard RGB video data of exposed human skin and extracting the heart-rate (HR) from the skin areas. Being a non-contact technique, VPPG has the potential to provide estimates of subject’s heart-rate, respiratory rate, and even the heart rate variability of human subjects with potential applications ranging from infant monitors, remote healthcare and psychological experiments, particularly given the non-contact and sensor-free nature of the technique. Though several previous studies have reported successful correlations in HR obtained using VPPG algorithms to HR measured using the gold-standard electrocardiograph, others have reported that these correlations are dependent on controlling for duration of the video-data analyzed, subject motion, and ambient lighting. Here, we investigate the ability of two commonly used VPPG-algorithms in extraction of human heart-rates under three different laboratory conditions. We compare the VPPG HR values extracted across these three sets of experiments to the gold-standard values acquired by using an electrocardiogram or a commercially available pulseoximeter. The two VPPG-algorithms were applied with and without KLT-facial feature tracking and detection algorithms from the Computer Vision MATLAB® toolbox. Results indicate that VPPG based numerical approaches have the ability to provide robust estimates of subject HR values and are relatively insensitive to the devices used to record the video data. However, they are highly sensitive to conditions of video acquisition including subject motion, the location, size and averaging techniques applied to regions-of-interest as well as to the number of video frames used for data processing.