An Experimentally Trained Noise Filtration Method of Optical Coherence Tomography Signals

Abstract

A method for wavelet filtration procedure training for optical coherence tomography (OCT) images using the experimental measurements of test objects that were constructed by means of water solutions of monodisperse nanoparticles and several microscopic inclusions has been described in the present paper. The choice of test-object parameters (concentration of water solution, size of nanoparticles, and shape, dimensions, and mutual position of inclusions) has allowed the modeling of various working conditions of OCT and setting different criteria for estimation of filtration efficiency. In the present work, the optimal filter for the considered example of a test object has been selected among the combinations of various basic functions of five wavelet families, soft and hard threshold filtering methods, four decomposition levels, and threshold values in a range of 0.05–3.05. The mutual position of the micro-inclusions has been used as a criterion for evaluating the filtration efficiency. As a result, it has been shown that the determined wavelet filter leads to effective suppression of the scattering noise in OCT images and preserve information about the structure of the object under study.