A fully conservative parallel numerical algorithm with adaptive spatial grid for solving nonlinear diffusion equations in image processing

In this paper we present simple yet efficient parallel program implementation of grid-difference method for solving nonlinear parabolic equations, which satisfies both fully conservative property and second order of approximation on non-uniform spatial grid according to geometrical sanity of a task. The proposed algorithm was tested on Perona–Malik method for image noise ltering task based on differential equations. Also in this work we propose generalization of the Perona–Malik equation, which is a one of diffusion in complex-valued region type. This corresponds to the conversion to such types of nonlinear equations like Leontovich–Fock equation with a dependent on the gradient field according to the nonlinear law coefficient of diffraction. This is a special case of generalization of the Perona–Malik equation to the multicomponent case. This approach makes noise removal process more flexible by increasing its capabilities, which allows achieving better results for the task of image denoising

Predictive models for COVID-19 detection using routine blood tests and machine learning

The problem of accurate, fast, and inexpensive COVID-19 tests has been urgent till now. Standard COVID-19 tests need high-cost reagents and specialized laboratories with high safety requirements, are time-consuming. Data of routine blood tests as a base of SARS-CoV-2 invasion detection allows using the most practical medicine facilities. But blood tests give general information about a patient’s state, which is not directly associated with COVID-19. COVID-19-specific features should be selected from the list of standard blood characteristics, and decision-making software based on appropriate clinical data should be created. This review describes the abilities to develop predictive models for COVID-19 detection using routine blood tests and machine learning

Raman spectroscopy of blood plasma for cancer diagnosis

Raman spectra of blood plasma were studied in the dynamics of the experimental glioma. We used a DXR Raman Microscope (Thermo Scientific), excitation wavelengths of 532 nm, range 80–3200 cm–1. Each sample of blood plasma was a droplet with a volume of 10 μL placed on a special aluminum plate. Machine learning methods were used to identify the most informative frequencies associated with cancer molecular markers. The most significant changes in the Raman spectra are observed in the 900–1700 cm–1 range

Imitation of optical coherence tomography images by wave Monte Carlo-based approach implemented with the Leontovich–Fock equation

We present a computational modeling approach for imitation of the time-domain optical coherence tomography (OCT) images of biotissues. The developed modeling technique is based on the implementation of the Leontovich–Fock equation into the wave Monte Carlo (MC) method. We discuss the benefits of the developed computational model in comparison to the conventional MC method based on the modeling of OCT images of a nevus. The developed model takes into account diffraction on bulk-absorbing microstructures and allows consideration of the influence of the amplitude–phase profile of the wave beam on the quality of the OCT images. The selection of optical parameters of modeling medium, used for simulation of optical radiation propagation in biotissues, is based on the results obtained experimentally by OCT. The developed computational model can be used for imitation of the light waves propagation both in time-domain and spectral-domain OCT approaches

Research on lymphedema by method of high-resolution multiphoton microscopy

In this paper results of iv-vivo measurements for healthy volunteers and people, with verified diagnosis of lymphedema, obtained with two-photon tomography with fluorescence lifetime imaging microscopy (FLIM) are presented. The papillary layer of the skin was analyzed at a depth of about 100 μm. The purpose of this study is to evaluate the lifetime of autofluorescence in the papillary dermis of healthy tissue and with the initial stage of lymphedema. In the course of the study, a small redistribution of autofluorescence lifetimes was observed for healthy volunteers and people with lymphedema disease. The research was carried out on the equipment of MPTflex (JenLab GmbH)

Application of multiphoton imaging and machine learning to lymphedema tissue analysis

The results of in-vivo two-photon imaging of lymphedema tissue are presented. The study involved 36 image samples from II stage lymphedema patients and 42 image samples from healthy volunteers. The papillary layer of the skin with a penetration depth of about 100 μm was examined. Both the collagen network disorganization and increase of the collagen/elastin ratio in lymphedema tissue, characterizing the severity of fibrosis, was observed. Various methods of image characterization, including edge detectors, a histogram of oriented gradients method, and a predictive model for diagnosis using machine learning, were used. The classification by “ensemble learning” provided 96% accuracy in validating the data from the testing set