Laser speckle contrast imaging and machine learning in application to physiological fluids flow rate recognition

Abstract

The laser speckle contrast imaging allows the determination of the flow motion in a sequence of images. The aim of this study is to combine the speckle contrast imaging and machine learning methods to recognition of physiological fluids flow rate. Data on the flow of intralipid with average flow rate of 0-2 mm/s in a glass capillary were obtained using a developed experimental setup. These data were used to train a feed-forward artificial neural network. The accuracy of random image recognition was quite low due to pulsations and the uneven flow set by the pump. To increase the recognition accuracy, various methods for calculating speckle contrast were used. The best result was obtained when calculating the mean spatial speckle contrast. The application of the mean spatial speckle contrast imaging together with the proposed artificial neural network allowed to increase the fluid flow rate recognition accuracy from about 65 % to 89 % and make it possible to exclude an expert from the data processing.