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Estimation of drinking water volume of laboratory animals based on image processing

This paper describes an image processing-based technique used to measure the volume of residual water in the drinking water bottle for the laboratory mouse. This technique uses a camera to capture the bottle's image and then processes the image to calculate the volume of water in the bottle. Fi...

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Autores principales: Liu, Zhihai, Liu, Feiyi, Zeng, Qingliang, Yin, Xiang, Yang, Yang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10219945/
https://www.ncbi.nlm.nih.gov/pubmed/37236974
http://dx.doi.org/10.1038/s41598-023-34460-w
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author Liu, Zhihai
Liu, Feiyi
Zeng, Qingliang
Yin, Xiang
Yang, Yang
author_facet Liu, Zhihai
Liu, Feiyi
Zeng, Qingliang
Yin, Xiang
Yang, Yang
author_sort Liu, Zhihai
collection PubMed
description This paper describes an image processing-based technique used to measure the volume of residual water in the drinking water bottle for the laboratory mouse. This technique uses a camera to capture the bottle's image and then processes the image to calculate the volume of water in the bottle. Firstly, the Grabcut method separates the foreground and background to avoid the influence of background on image feature extraction. Then Canny operator was used to detect the edge of the water bottle and the edge of the liquid surface. The cumulative probability Hough detection identified the water bottle edge line segment and the liquid surface line segment from the edge image. Finally, the spatial coordinate system is constructed, and the length of each line segment on the water bottle is calculated by using plane analytical geometry. Then the volume of water is calculated. By comparing image processing time, the pixel number of liquid level, and other indexes, the optimal illuminance and water bottle color were obtained. The experimental results show that the average deviation rate of this method is less than 5%, which significantly improves the accuracy and efficiency of measurement compared with traditional manual measurement.
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spelling pubmed-102199452023-05-28 Estimation of drinking water volume of laboratory animals based on image processing Liu, Zhihai Liu, Feiyi Zeng, Qingliang Yin, Xiang Yang, Yang Sci Rep Article This paper describes an image processing-based technique used to measure the volume of residual water in the drinking water bottle for the laboratory mouse. This technique uses a camera to capture the bottle's image and then processes the image to calculate the volume of water in the bottle. Firstly, the Grabcut method separates the foreground and background to avoid the influence of background on image feature extraction. Then Canny operator was used to detect the edge of the water bottle and the edge of the liquid surface. The cumulative probability Hough detection identified the water bottle edge line segment and the liquid surface line segment from the edge image. Finally, the spatial coordinate system is constructed, and the length of each line segment on the water bottle is calculated by using plane analytical geometry. Then the volume of water is calculated. By comparing image processing time, the pixel number of liquid level, and other indexes, the optimal illuminance and water bottle color were obtained. The experimental results show that the average deviation rate of this method is less than 5%, which significantly improves the accuracy and efficiency of measurement compared with traditional manual measurement. Nature Publishing Group UK 2023-05-26 /pmc/articles/PMC10219945/ /pubmed/37236974 http://dx.doi.org/10.1038/s41598-023-34460-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Liu, Zhihai
Liu, Feiyi
Zeng, Qingliang
Yin, Xiang
Yang, Yang
Estimation of drinking water volume of laboratory animals based on image processing
title Estimation of drinking water volume of laboratory animals based on image processing
title_full Estimation of drinking water volume of laboratory animals based on image processing
title_fullStr Estimation of drinking water volume of laboratory animals based on image processing
title_full_unstemmed Estimation of drinking water volume of laboratory animals based on image processing
title_short Estimation of drinking water volume of laboratory animals based on image processing
title_sort estimation of drinking water volume of laboratory animals based on image processing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10219945/
https://www.ncbi.nlm.nih.gov/pubmed/37236974
http://dx.doi.org/10.1038/s41598-023-34460-w
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