The present study aimed at investigating the relationship between tablet hardness and homogeneity of different Yinhuang dispersible tablets by near-infrared chemical imaging (NIR-CI) technology. The regularity of best hardness was founded between tablet hardness and the spatial distribution uniformity of Yinhuang dispersible tablets. The ingredients homogeneity of Yinhuang dispersible tablets could be spatially determined using basic analysis of correlation between analysis (BACRA) method and binary image. Then different hardnesses of Yinhuang dispersible tablets were measured. Finally, the regularity between tablet hardness and the spatial distribution uniformity of Yinhuang dispersible tablets was illuminated by quantifying the agglomerate of polyvinyl poly pyrrolidone (PVPP). The result demonstrated that the distribution of PVPP was unstable when the hardness was too large or too small, while the agglomerate of PVPP was smaller and more stable when the best tablet hardness was 75 N. This paper provided a novel methodology for selecting the best hardness in the tabletting process of Chinese Medicine Tablet.

Optical coherence tomography (OCT) is employed in the diagnosis of skin cancer. Particularly, quantitative image features extracted from OCT images might be used as indicators to classify the skin tumors. In the present paper, we investigated intensity-based, texture-based and fractalbased features for automatically classifying the melanomas, basal cell carcinomas and pigment nevi. Generalized estimating equations were used to test for differences between the skin tumors. A modified p value of <0.001 was considered statistically significant. Significant increase of mean and median of intensity and significant decrease of mean and median of absolute gradient were observed in basal cell carcinomas and pigment nevi as compared with melanomas. Significant decrease of contrast, entropy and fractal dimension was also observed in basal cell carcinomas and pigment nevi as compared with melanomas. Our results suggest that the selected quantitative image features of OCT images could provide useful information to differentiate basal cell carcinomas and pigment nevi from the melanomas. Further research is warranted to determine how this approach may be used to improve the classification of skin tumors.

Protein-directed fluorescent Au nanoclusters have been widely studied owing to their potential applications in sensing, imaging, and drug and gene delivery. However, the use of nanoclusters in drug delivery is limited by low cellular uptake. In this study, human serum albumin-directed Au nanoclusters served as building blocks to obtain protein nanoparticles by desolvation. The nanoparticles had a decent quantum yield (QY), high colloidal stability and low cytotoxicity, and they could be readily conjugated with biological molecules. The cellular uptake of the Au nanoclusters and nanocluster-loaded protein nanoparticles were studied by confocal fluorescence microscopy. Agglomeration of the protein-directed Au nanoclusters into 50–150-nm nanoparticles dramatically increased the cellular uptake.

In this paper, the mathematical model of distribution of the injected compound in biological liquid flow has been described. It is considered that biological liquid contains a few phases such as water, peptides and cells. The injected compound (for example, photosensitizer) can interact with peptides and cells. At the time, viscosity of the biological liquid depends on pathology present in organism. The obtained distribution of the compound connects on changes of its fluorescence spectra which are registered during fluorescent diagnostics of tumors. It is obtained that the curves do not have monotonic nature. There is a sharp curves decline in the first few seconds after injection. Intensivity of curves rises after decreasing. It is especially pronounced for wavelength 590 nm and 580 nm (near the \transparency window" of biological tissues). Time of in- flection point shifts from 8.4 s to 6.9 s for longer wavelength. However, difference between curves is little for different viscosity means of the biological liquid. Thus, additional pathology present in organism does not impact to the results of in vivo biomedical investigations.

Diffuse Optical Spectroscopy (DOS) is a promising non-invasive and non-ionizing technique for breast anomaly detection. In this study, we have developed a new handheld DOS probe to measure optical properties of breast tissue. In the proposed probe, the breast tissue is illuminated with four near infrared (NIR) wavelengths light emitting diodes (LED), which are encapsulated in a package (eLEDs), and two PIN photodiodes measure the intensity of the scattered photons at two different locations. The proposed technique of using eLEDs is introduced, in order to have a multi-wavelength pointed-beam illumination source instead of using the laser-coupled fiberoptic technique, which increases the complexity, size, and cost of the probe. Despite the fact that the proposed technique miniaturizes the probe and reduces the complexity of the DOS, the study proves that it is accurate and reliable in measuring optical properties of the tissue. The measurements are performed at the rate of 10 Hz which is suitable for dynamic measurement of biological activity, in-vivo. The multi-spectral evaluation algorithm is used to reconstruct four main absorber concentrations in the breast including oxy-hemoglobin (cHb), deoxy-hemoglobin (cHbO₂), water (cH₂O), fat (cFat), and average scattering coefficient of the medium, as well as concentration changes in Hb (△cHb) and HbO₂ (△cHbO₂). Although the probe is designed for breast cancer diagnosis, it can be used in a wide range of applications for both static and dynamic measurements such as functional brain imaging. A series of phantoms, comprised of Delrinr, Intralipidr, PierceTM and Black ink, are used to verify performance of the device. The probe will be tested on human subjects, in-vivo, in the next phase.

With the introduction of spectral-domain optical coherence tomography (SD-OCT), much larger image datasets are routinely acquired compared to what was possible using the previous generation of time-domain OCT. Thus, there is a critical need for the development of three-dimensional (3D) segmentation methods for processing these data. We present here a novel 3D automatic segmentation method for retinal OCT volume data. Briefly, to segment a boundary surface, two OCT volume datasets are obtained by using a 3D smoothing filter and a 3D differential filter. Their linear combination is then calculated to generate new volume data with an enhanced boundary surface, where pixel intensity, boundary position information, and intensity changes on both sides of the boundary surface are used simultaneously. Next, preliminary discrete boundary points are detected from the A-Scans of the volume data. Finally, surface smoothness constraints and a dynamic threshold are applied to obtain a smoothed boundary surface by correcting a small number of error points. Our method can extract retinal layer boundary surfaces sequentially with a decreasing search region of volume data. We performed automatic segmentation on eight human OCT volume datasets acquired from a commercial Spectralis OCT system, where each volume of datasets contains 97 OCT B-Scan images with a resolution of 496 × 512 (each B-Scan comprising 512 A-Scans containing 496 pixels); experimental results show that this method can accurately segment seven layer boundary surfaces in normal as well as some abnormal eyes.

A compact volume holographic imaging (VHI) method that can detect fluorescence objects located in diffusive medium in spectral selective imaging manner is presented. The enlargement of lateral field of view of the VHI system is realized by using broadband illumination and demagni fication optics. Each target spectrum of fluorescence emitting from a diffusive medium is probed by tuning the inclination angle of the transmission volume holographic grating (VHG). With the use of the single transmission VHG, fluorescence images with different spectrum are obtained sequentially and precise three-dimensional (3D) information of deep fluorescent objects located in a diffusive medium can be reconstructed from these images. The results of phantom experiments demonstrate that two fluorescent objects with a sub-millimeter distance can be resolved by spectral selective imaging.

Preclinical research of biomedical optoelectronic devices is often performed with the use of blood phantoms — a simplified physical model of blood. The aim of this study is the comparison and distinction between blood phantoms as well as whole human blood measurements. We show how the use of such phantoms may influence the incorrect interpretation of measured signal. On the other hand, we highlight how the use of blood phantoms enables to investigate the phenomena that otherwise are almost impossible to be noticed.

The content of berberine hydrochloride (BH) in compound berberine tablets (CBTs) is subject to strict requirements. Its content is usually measured based on chemical analysis. In this paper, the fluorescence spectral imaging method was used to study the relative content of BH from a physics perspective. By comparing the relative fluorescence intensity of self-made CBTs with different mass percentages of BH, a linear positive relationship was observed between the BH content and the relative fluorescence intensity, and accordingly the quality of CBTs of different brands was evaluated. The results indicate that the fluorescence spectral imaging method can be a simple, fast and nondestructive semi-quantitative analysis method to determine the content of BH in CBTs, and this method has great potential in the quality control of CBTs.

In optical techniques, noise signal is a classical problem in medical image processing. Recently, there has been considerable interest in using the wavelet transform with Bayesian estimation as a powerful tool for recovering image from noisy data. In wavelet domain, if Bayesian estimator is used for denoising problem, the solution requires a prior knowledge about the distribution of wavelet coefficients. Indeed, wavelet coefficients might be better modeled by super Gaussian density. The super Gaussian density can be generated by Gaussian scale mixture (GSM). So, we present new minimum mean square error (MMSE) estimator for spherically-contoured GSM with Maxwell distribution in additive white Gaussian noise (AWGN). We compare our proposed method to current state-of-the-art method applied on standard test image and we quantify achieved performance improvement.