Fast and precise image generation of blood vessels embedded in skin

Radiative transfer equation-based color prediction and color adjustment strategies

This paper discusses different strategies for color prediction and matching. Although many groups use the two-flux model (i.e., the Kubelka-Munk theory or its extensions), we introduce a solution of the P _N approximation for the radiative transfer equation (RTE) with modified Mark boundaries for the prediction of the transmittance and reflectance of turbid slabs with or without a glass layer on top. To demonstrate the capabilities of our solution, we have presented a way to prepare samples with different scatterers and absorbers where we can control and predict the optical properties and discussed three color-matching strategies: the approximation of the scattering and absorption coefficient, the adjustment of the reflectance, and the direct matching of the color valueL ^∗ a ^∗ b ^∗.

Single scattering models for radiative transfer of isotropic and cone-shaped light sources in fog

The simulation of rare edge cases such as adverse weather conditions is the enabler for the deployment of the next generation of autonomous drones and vehicles into conditions where human operation is error-prone. Therefore, such settings must be simulated as accurately as possible and be computationally efficient, so to allow the training of deep learning algorithms for scene understanding, which require large-scale datasets disallowing extensive Monte Carlo simulations. One computationally-expensive step is the simulation of light sources in scattering media, which can be tackled by the radiative transfer equation and approximated by analytical solutions in the following. Traditionally, a single scattering event is assumed for fog rendering, since it is the dominant effect for relatively low scattering media. This assumption allows us to present an improved solution to calculate the so called air-light integral that can be evaluated fast and robustly for an isotropic point source in homogeneous media. Additionally, the solution is extended for a cone-shaped source and implemented in a computer vision rendering pipeline fulfilling computational restrictions for deep learning uses. All solutions can handle arbitrary azimuthally symmetric phase functions and were tested with the Henyey-Greenstein phase function and an advection fog phase function calculated from a particle distribution using Mie's theory. The used approximations are validated through extensive Monte Carlo simulations and the solutions are used to augment good weather images towards inclement conditions with focus on visible light sources, so to provide additional data in such hard-to-collect settings.

Visualizing veins from color images under varying illuminations for medical applications

SIGNIFICANCE

Effective vein visualization is critically important for several clinical procedures, such as venous blood sampling and intravenous injection. Existing technologies using infrared device or ultrasound rely on professional equipment and are not suitable for daily medical care. A regression-based vein visualization method is proposed.

AIM

We visualize veins from conventional RGB images to provide assistance in venipuncture procedures as well as clinical diagnosis of some venous insufficiency.

APPROACH

The RGB images taken by digital cameras are first transformed to spectral reflectance images using Wiener estimation. Multiple regression analysis is then applied to derive the relationship between spectral reflectance and the concentrations of pigments. Monte Carlo simulation is adopted to get prior information. Finally, vein patterns are visualized from the spatial distribution of pigments. To minimize the effect of illumination on skin color, light correction and shading removal operations are performed in advance.

RESULTS

Experimental results from inner forearms of 60 subjects show the effectiveness of the regression-based method. Subjective and objective evaluations demonstrate that the clarity and completeness of vein patterns can be improved by light correction and shading removal.

CONCLUSIONS

Vein patterns can be successfully visualized from RGB images without any professional equipment. The proposed method can assist in venipuncture procedures. It also shows promising potential to be used in clinical diagnosis and treatment of some venous insufficiency.

Blood glucose detection based on Teager-Kaiser main energy of photoacoustic signal

Real-time blood glucose detection is an essential tool for diabetes monitoring. Non-invasive blood glucose detection technology is one of the current research hotspots in this field. Previous research mainly focused on improving the system's detection capability to obtain signals with low signal-to-noise ratio and high quality, and simple methods are often used in signal processing. Moreover, photoacoustic signal simulation also simplifies the influence of the transmission medium on the signal. In the present study, we built a new simulation model which considers human skin, blood, and the detector's limitations, to obtain a more practical photoacoustic signal. We then proposed a blood glucose detection algorithm based on Teager-Kaiser main energy (TKME) to overcome noise and medium interference and achieve a high detection accuracy at low SNR. Finally, the simulation and actual data were utilised during the experiment, and the detection error was 15 mg/dL (SNR = 10 dB).