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


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.

Publication DOI:
Divisions: College of Engineering & Physical Sciences > Aston Institute of Photonics Technology (AIPT)
College of Engineering & Physical Sciences
Funding Information: This work was supported by the Russian Science Foundation under the Project 20-79-00332.
Additional Information: Copyright © 2021 Ivan Stebakov, et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Funding: This work was supported by the Russian Science Foundation under the Project 20-79-00332.
Uncontrolled Keywords: Artificial neural network,Flow rate,Laser speckle contrast imaging,Physiological fluid,Rheology,Electrical and Electronic Engineering,General,Control and Systems Engineering,Mechanical Engineering
Publication ISSN: 2345-0533
Last Modified: 28 May 2024 07:32
Date Deposited: 30 Jul 2021 12:46
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://www.jve ... m/article/22013 (Publisher URL)
PURE Output Type: Conference article
Published Date: 2021-06-28
Accepted Date: 2021-05-23
Authors: Stebakov, Ivan
Kornaeva, Elena
Stavtsev, Dmitry
Potapova, Elena
Dremin, Viktor (ORCID Profile 0000-0001-6974-3505)



Version: Published Version

License: Creative Commons Attribution

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