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Sensors (Basel, Switzerland)2021; 22(1); 191; doi: 10.3390/s22010191

Advances in Thermal Image Analysis for the Detection of Pregnancy in Horses Using Infrared Thermography.

Abstract: Infrared thermography (IRT) was applied as a potentially useful tool in the detection of pregnancy in equids, especially native or wildlife. IRT measures heat emission from the body surface, which increases with the progression of pregnancy as blood flow and metabolic activity in the uterine and fetal tissues increase. Conventional IRT imaging is promising; however, with specific limitations considered, this study aimed to develop novel digital processing methods for thermal images of pregnant mares to detect pregnancy earlier with higher accuracy. In the current study, 40 mares were divided into non-pregnant and pregnant groups and imaged using IRT. Thermal images were transformed into four color models (RGB, YUV, YIQ, HSB) and 10 color components were separated. From each color component, features of image texture were obtained using Histogram Statistics and Grey-Level Run-Length Matrix algorithms. The most informative color/feature combinations were selected for further investigation, and the accuracy of pregnancy detection was calculated. The image texture features in the RGB and YIQ color models reflecting increased heterogeneity of image texture seem to be applicable as potential indicators of pregnancy. Their application in IRT-based pregnancy detection in mares allows for earlier recognition of pregnant mares with higher accuracy than the conventional IRT imaging technique.
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Publication Date: 2021-12-28 PubMed ID: 35009733PubMed Central: PMC8749616DOI: 10.3390/s22010191Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The researchers in this study explored the use of infrared thermography (IRT) to detect pregnancy in horses, especially native or wild ones. They not only used IRT in a conventional method, but they also worked to improve its accuracy and timing through the novel digital processing of thermal images. They found specific features in certain color models which were identified as potential indicators of pregnancy.

Study Design and Method

  • The study involved 40 mares that were split into two groups: pregnant and non-pregnant. Infrared thermography (IRT), a tool which measures heat emission from the body surface, was used to capture images.
  • The images resulting from this process were then converted into four different color models: RGB, YUV, YIQ, and HSB. Ten color components were subsequently separated from these models.
  • In order to extract as many useful features as possible, the researchers utilized Histogram Statistics and Grey-Level Run-Length Matrix algorithms. These algorithms processed the image texture information derived from each color component.

Results

  • In their analysis, the researchers identified the most informative color and feature combinations and proceeded to calculate the accuracy of pregnancy detection using these identifiers.
  • They found that the texture features in the RGB (Red, Green, Blue) and YIQ (a color space used by the NTSC color TV system) color models were particularly useful. These showed an increased heterogeneity of image texture, which the researchers identified as potential indicators of pregnancy.

Conclusion

  • The researchers concluded that through this digital process, infrared thermography can be used to detect pregnancy in horses, and especially in wild ones, with a higher degree of accuracy than the conventional method.
  • Moreover, this method allows for earlier detection of pregnancy, enhancing the overall effectiveness and usefulness of infrared thermography in equine pregnancy detection.

Cite This Article

APA
Domino M, Borowska M, Kozłowska N, Zdrojkowski Ł, Jasiński T, Smyth G, Maśko M. (2021). Advances in Thermal Image Analysis for the Detection of Pregnancy in Horses Using Infrared Thermography. Sensors (Basel), 22(1), 191. https://doi.org/10.3390/s22010191

Publication

Sensors (Basel, Switzerland)
ISSN: 1424-8220
NlmUniqueID: 101204366
Country: Switzerland
Language: English
Volume: 22
Issue: 1
PII: 191

Researcher Affiliations

Domino, Małgorzata
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences, 02-787 Warsaw, Poland.
Borowska, Marta
  • Institute of Biomedical Engineering, Faculty of Mechanical Engineering, Białystok University of Technology, 15-351 Bialystok, Poland.
Kozłowska, Natalia
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences, 02-787 Warsaw, Poland.
Zdrojkowski, Łukasz
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences, 02-787 Warsaw, Poland.
Jasiński, Tomasz
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences, 02-787 Warsaw, Poland.
Smyth, Graham
  • Menzies Health Institute Queensland, Griffith University School of Medicine, Southport, QLD 4222, Australia.
Maśko, Małgorzata
  • Department of Animal Breeding, Institute of Animal Science, Warsaw University of Life Sciences, 02-787 Warsaw, Poland.

MeSH Terms

  • Algorithms
  • Animals
  • Female
  • Horses
  • Image Processing, Computer-Assisted
  • Pregnancy
  • Thermography
  • Uterus

Grant Funding

  • 2019/03/X/NZ9/01759 / National Science Center
  • WI/WM-IIB/2/2021 / Ministry of Science and Higher Education

Conflict of Interest Statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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