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

Autor: Małgorzata Domino, Marta Borowska, Natalia Kozłowska, Anna Trojakowska, Łukasz Zdrojkowski, Tomasz Jasiński, Graham Smyth, Małgorzata Maśko
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Animals, Vol 12, Iss 4, p 444 (2022)
Druh dokumentu: article
ISSN: 12040444
2076-2615
DOI: 10.3390/ani12040444
Popis: 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.
Databáze: Directory of Open Access Journals
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