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Animals : an open access journal from MDPI2022; 12(4); doi: 10.3390/ani12040444

Selection of Image Texture Analysis and Color Model in the Advanced Image Processing of Thermal Images of Horses following Exercise.

Abstract: As the detection of horse state after exercise is constantly developing, a link between blood biomarkers and infrared thermography (IRT) was investigated using advanced image texture analysis. The aim of the study was to determine which combinations of RGB (red-green-blue), YUI (brightness-UV-components), YIQ (brightness-IQ-components), and HSB (hue-saturation-brightness) color models, components, and texture features are related to the blood biomarkers of exercise effect. Twelve Polish warmblood horses underwent standardized exercise tests for six consecutive days. Both thermal images and blood samples were collected before and after each test. All 144 obtained IRT images were analyzed independently for 12 color components in four color models using eight texture-feature approaches, including 88 features. The similarity between blood biomarker levels and texture features was determined using linear regression models. In the horses' thoracolumbar region, 12 texture features (nine in RGB, one in YIQ, and two in HSB) were related to blood biomarkers. Variance, sum of squares, and sum of variance in the RGB were highly repeatable between image processing protocols. The combination of two approaches of image texture (histogram statistics and gray-level co-occurrence matrix) and two color models (RGB, YIQ), should be considered in the application of digital image processing of equine IRT.
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Publication Date: 2022-02-12 PubMed ID: 35203152PubMed Central: PMC8868218DOI: 10.3390/ani12040444Google 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.

This study explores the relationship between blood biomarkers and infrared thermal images (IRT) after exercise in horses, with the aim of determining which combinations of various color models and texture features best correlate with these biomarkers. The team analyzed 144 thermal images from 12 Polish warmblood horses using eight texture methods and four color models. The results suggest that certain texture features in the RGB and YIQ color models showed strong relationships with blood biomarkers, suggesting they could be important for digital processing of equine thermal images.

Study Methodology

  • The research involved twelve Polish warmblood horses which were subjected to standardized exercise tests for six consecutive days.
  • Thermal images and blood samples were taken both before and after each exercise test. In total, 144 infrared thermal images were taken and analyzed.
  • The images were independently analyzed using four different color models – RGB (red-green-blue), YUI (brightness-UV-components), YIQ (brightness-IQ-components), and HSB (hue-saturation-brightness).
  • The textures in these images were assessed using eight different texture-feature approaches, providing a total of 88 different features.

Data Analysis and Results

  • The researchers compared the blood biomarker levels to the texture features using linear regression models. This helped understand the correlation between the blood biomarkers and the image texture features.
  • In the thoracolumbar region of the horses, 12 texture features were found to be positively correlated with the blood biomarkers. Out of these, nine were found in the RGB color model, one in YIQ, and two in HSB color model.
  • Features such as variance, sum of squares, and sum of variance in the RGB color model were highly repeatable across different image processing protocols.

Conclusion

  • The study concluded that for examining equine IRT images, the combination of two image texture approaches (histogram statistics and gray-level co-occurrence matrix), and two color models (RGB and YIQ) should be used.
  • This finding enhances the application of digital image processing in equine infrared thermal imaging can help to better evaluate the horse’s state after exercise.
  • The improved consistency and precision allow for better monitoring and detection of any changes or potential issues this may have implications for training and injury prevention strategies.

Cite This Article

APA
Domino M, Borowska M, Kozłowska N, Trojakowska A, Zdrojkowski Ł, Jasiński T, Smyth G, Maśko M. (2022). Selection of Image Texture Analysis and Color Model in the Advanced Image Processing of Thermal Images of Horses following Exercise. Animals (Basel), 12(4). https://doi.org/10.3390/ani12040444

Publication

Animals : an open access journal from MDPI
ISSN: 2076-2615
NlmUniqueID: 101635614
Country: Switzerland
Language: English
Volume: 12
Issue: 4

Researcher Affiliations

Domino, Małgorzata
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences (WULS-SGGW), 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 (WULS-SGGW), 02-787 Warsaw, Poland.
Trojakowska, Anna
  • The Scientific Society of Veterinary Medicine Students, Warsaw University of Life Sciences (WULS-SGGW), 02-787 Warsaw, Poland.
Zdrojkowski, Łukasz
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences (WULS-SGGW), 02-787 Warsaw, Poland.
Jasiński, Tomasz
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences (WULS-SGGW), 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 (WULS-SGGW), 02-787 Warsaw, Poland.

Grant Funding

  • WI/WM-IIB/2/2021 / Ministry of Science and Higher Education

Conflict of Interest Statement

The authors declare no conflict of interest.

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Citations

This article has been cited 6 times.
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