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

Classification of patients with broncho-pulmonary diseases based on analysis of absorption spectra of exhaled air samples with SVM and neural network algorithm application

In this work results of classification of patients with broncho-pulmonary diseases based on analysis of exhaled air samples are presented. These results obtained by application of laser photoacoustic spectroscopy method and intellectual data analysis ones (Principal Component Analysis, Support vector machines, neural networks). Absorption spectra of exhaled air of gathered volunteers were registered; data preparation for classification procedure of absorption spectra of exhaled air of healthy and sick people was made. Also error matrices for neural networks and sensitivity/specificity values in case of classification with SVM method were obtained. This work was partially supposed by the Federal Target Program for Research and Development, Contract No. 14.578.21.0082 (unique identifier of applied scientific research and experimental development RFMEFI57814X0082)

Kalman filtering in the problem of noise reduction in the absorption spectra of exhaled air

We examined possibilities of the Kalman filter for reducing the noise effects in the analysis of absorption spectra of gas samples, in particular, for samples of the exhaled air. It has been shown that when comparing groups of patients with broncho-pulmonary diseases on the basis of the absorption spectra analysis of exhaled air samples the data preprocessing with the Kalman filtering can improve the classification sensitivity using a support vector kernel with mpl

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

Imitation of ultra-sharp light focusing within turbid tissue-like scattering medium by using time-independent Helmholtz equation and method Monte Carlo

Based on time-independent Helmholtz equation and its solution in frame of inhomogeneous approximation a hybrid computational method for imitation of propagation of bounded laser beam focused into biological tissue is introduced. The biological tissue is simulated as a semi-infinite randomly inhomogeneous medium. The developed approach is intended to model laser beams in the super-sharp focusing mode. The results of modeling of laser light focusing into the turbid tissue-like scattering medium with lenses of various shapes are presented

Applications of principal component analysis to breath air absorption spectra profiles classification

The results of numerical simulation of application principal component analysis to absorption spectra of breath air of patients with pulmonary diseases are presented. Various methods of experimental data preprocessing are analyzed

Classification of patients with broncho-pulmonary diseases based on analysis of absorption spectra of exhaled air samples with SVM and neural network algorithm application

In this work results of classification of patients with broncho-pulmonary diseases based on analysis of exhaled air samples are presented. These results obtained by application of laser photoacoustic spectroscopy method and intellectual data analysis ones (Principal Component Analysis, Support vector machines, neural networks). Absorption spectra of exhaled air of gathered volunteers were registered; data preparation for classification procedure of absorption spectra of exhaled air of healthy and sick people was made. Also error matrices for neural networks and sensitivity/specificity values in case of classification with SVM method were obtained. This work was partially supposed by the Federal Target Program for Research and Development, Contract No. 14.578.21.0082 (unique identifier of applied scientific research and experimental development RFMEFI57814X0082)