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.
| Original language | English |
|---|---|
| Pages (from-to) | 50-55 |
| Number of pages | 6 |
| Journal | Vibroengineering Procedia |
| Volume | 38 |
| DOIs | |
| Publication status | Published - 28 Jun 2021 |
| Event | 52nd International Conference on Vibroengineering - St. Petersburg, Russian Federation Duration: 28 Jun 2021 → 30 Jun 2021 |
Bibliographical note
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.
Keywords
- Artificial neural network
- Flow rate
- Laser speckle contrast imaging
- Physiological fluid
- Rheology