Abstract:The infrared thermography is a diagnostic imaging tool, which measures the surface temperature of an object through its heat emission. It is a non-invasive method
“… Fig. 2 The methods chosen for classic and advanced analysis of the thoracic region of the horse’s back: a Schweinitz [ 2 ] and Fonseca et al [ 6 ] method I; b Tunley and Henson [ 14 ] method II; c Soroko et al [ 15 ] method III; d Pavelski et al [ 16 ] method IV; e Masko et al [ 12 ] method V; f novel texture analysis method …”
Section: Methodsmentioning
confidence: 99%
“…The method IV was conducted by Pavelski et al [ 16 ] and measured the maximal, minimal and average temperatures in two selected quadrate of the thermographic image from the thoracic region of the horse’s back. The quadrates were the selected area of 20X20 pixels of the thoracic region (Ar), both sides left (ArL) and right (ArR).…”
Section: Methodsmentioning
confidence: 99%
“…The common prevalence of back problems in riding horses prompted the popularization of thermography of the thoracolumbar region [ 2 , 6 , 12 , 14 – 16 ]. These measurements of SBT were used in horses to identify and localize spine-related diseases [ 2 ], to diagnose thoracolumbar lesions in equine athletes [ 6 , 15 ], to evaluate a normal thermal pattern [ 14 , 16 ], as well as to describe interactions between horse and rider [ 12 ].…”
Section: Introductionmentioning
confidence: 99%
“…The common prevalence of back problems in riding horses prompted the popularization of thermography of the thoracolumbar region [ 2 , 6 , 12 , 14 – 16 ]. These measurements of SBT were used in horses to identify and localize spine-related diseases [ 2 ], to diagnose thoracolumbar lesions in equine athletes [ 6 , 15 ], to evaluate a normal thermal pattern [ 14 , 16 ], as well as to describe interactions between horse and rider [ 12 ]. Each of the cited studies has applied a different analysis method to interpret the superficial body temperature of the thoracic region of a horse’s back e.g., looking for thermographically portrayed “hot spots” or “cold regions” [ 2 , 6 ], analyzing the range of temperatures for the back measured along three horizontal lines [ 14 ], comparing the average temperatures measured in three areas separated from the back surface [ 15 ], measuring the average temperature in the selected quadrate of the thermographic image from the thoracic region of the horse’s back [ 16 ] or evaluating the heat pattern of the thoracolumbar area using 37 reference points grouped into 7 regions of interest [ 12 ].…”
Background
The horses’ backs are particularly exposed to overload and injuries due to direct contact with the saddle and the influence of e.g. the rider’s body weight. The maximal load for a horse’s back during riding has been suggested not to exceed 20% of the horses’ body weight. The common prevalence of back problems in riding horses prompted the popularization of thermography of the thoracolumbar region. However, the analysis methods of thermographic images used so far do not distinguish loaded horses with body weight varying between 10 and 20%.
Results
The superficial body temperature (SBT) of the thoracolumbar region of the horse’s back was imaged using a non-contact thermographic camera before and after riding under riders with LBW (low body weight, 10%) and HBW (high body weight, 15%). Images were analyzed using six methods: five recent SBT analyses and the novel approach based on Gray Level Co-Occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM). Temperatures of the horse’s thoracolumbar region were higher (p < 0.0001) after then before the training, and did not differ depending on the rider’s body weight (p > 0.05), regardless of used SBT analysis method. Effort-dependent differences (p < 0.05) were noted for six features of GLCM and GLRLM analysis. The values of selected GLCM and GLRLM features also differed (p < 0.05) between the LBW and HBW groups.
Conclusion
The GLCM and GLRLM analyses allowed the differentiation of horses subjected to a load of 10 and 15% of their body weights while horseback riding in contrast to the previously used SBT analysis methods. Both types of analyzing methods allow to differentiation thermal images obtained before and after riding. The textural analysis, including selected features of GLCM or GLRLM, seems to be promising tools in considering the quantitative assessment of thermographic images of horses’ thoracolumbar region.
“… Fig. 2 The methods chosen for classic and advanced analysis of the thoracic region of the horse’s back: a Schweinitz [ 2 ] and Fonseca et al [ 6 ] method I; b Tunley and Henson [ 14 ] method II; c Soroko et al [ 15 ] method III; d Pavelski et al [ 16 ] method IV; e Masko et al [ 12 ] method V; f novel texture analysis method …”
Section: Methodsmentioning
confidence: 99%
“…The method IV was conducted by Pavelski et al [ 16 ] and measured the maximal, minimal and average temperatures in two selected quadrate of the thermographic image from the thoracic region of the horse’s back. The quadrates were the selected area of 20X20 pixels of the thoracic region (Ar), both sides left (ArL) and right (ArR).…”
Section: Methodsmentioning
confidence: 99%
“…The common prevalence of back problems in riding horses prompted the popularization of thermography of the thoracolumbar region [ 2 , 6 , 12 , 14 – 16 ]. These measurements of SBT were used in horses to identify and localize spine-related diseases [ 2 ], to diagnose thoracolumbar lesions in equine athletes [ 6 , 15 ], to evaluate a normal thermal pattern [ 14 , 16 ], as well as to describe interactions between horse and rider [ 12 ].…”
Section: Introductionmentioning
confidence: 99%
“…The common prevalence of back problems in riding horses prompted the popularization of thermography of the thoracolumbar region [ 2 , 6 , 12 , 14 – 16 ]. These measurements of SBT were used in horses to identify and localize spine-related diseases [ 2 ], to diagnose thoracolumbar lesions in equine athletes [ 6 , 15 ], to evaluate a normal thermal pattern [ 14 , 16 ], as well as to describe interactions between horse and rider [ 12 ]. Each of the cited studies has applied a different analysis method to interpret the superficial body temperature of the thoracic region of a horse’s back e.g., looking for thermographically portrayed “hot spots” or “cold regions” [ 2 , 6 ], analyzing the range of temperatures for the back measured along three horizontal lines [ 14 ], comparing the average temperatures measured in three areas separated from the back surface [ 15 ], measuring the average temperature in the selected quadrate of the thermographic image from the thoracic region of the horse’s back [ 16 ] or evaluating the heat pattern of the thoracolumbar area using 37 reference points grouped into 7 regions of interest [ 12 ].…”
Background
The horses’ backs are particularly exposed to overload and injuries due to direct contact with the saddle and the influence of e.g. the rider’s body weight. The maximal load for a horse’s back during riding has been suggested not to exceed 20% of the horses’ body weight. The common prevalence of back problems in riding horses prompted the popularization of thermography of the thoracolumbar region. However, the analysis methods of thermographic images used so far do not distinguish loaded horses with body weight varying between 10 and 20%.
Results
The superficial body temperature (SBT) of the thoracolumbar region of the horse’s back was imaged using a non-contact thermographic camera before and after riding under riders with LBW (low body weight, 10%) and HBW (high body weight, 15%). Images were analyzed using six methods: five recent SBT analyses and the novel approach based on Gray Level Co-Occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM). Temperatures of the horse’s thoracolumbar region were higher (p < 0.0001) after then before the training, and did not differ depending on the rider’s body weight (p > 0.05), regardless of used SBT analysis method. Effort-dependent differences (p < 0.05) were noted for six features of GLCM and GLRLM analysis. The values of selected GLCM and GLRLM features also differed (p < 0.05) between the LBW and HBW groups.
Conclusion
The GLCM and GLRLM analyses allowed the differentiation of horses subjected to a load of 10 and 15% of their body weights while horseback riding in contrast to the previously used SBT analysis methods. Both types of analyzing methods allow to differentiation thermal images obtained before and after riding. The textural analysis, including selected features of GLCM or GLRLM, seems to be promising tools in considering the quantitative assessment of thermographic images of horses’ thoracolumbar region.
“…Following the methodology of previous equine researchers, body surface temperatures in healthy animals were evaluated. The normal thermal image was already described, for e.g., the coronary band [28], distal forelimb joints [26,29], the thoracolumbar region [30], the back, and pelvic regions [31] in the horse. It showed a high degree of symmetry between the left and right sides of the body [1,26] and reproducibility over hourly, daily, and weekly intervals up to 90% [30].…”
Infrared thermography (IRT) is a valuable diagnostic tool in equine veterinary medicine; however, little is known about its application to donkeys. This study aims to find patterns in thermal images of donkeys and horses and determine if these patterns share similarities. The study is carried out on 18 donkeys and 16 horses. All equids undergo thermal imaging with an infrared camera and measurement of the skin thickness and hair coat length. On the class maps of each thermal image, fifteen regions of interest (ROIs) are annotated and then combined into 10 groups of ROIs (GORs). The existence of statistically significant differences between surface temperatures in GORs is tested both “globally” for all animals of a given species and “locally” for each animal. Two special cases of animals that differed from the rest are also discussed. The results indicate that the majority of thermal patterns are similar for both species; however, average surface temperatures in horses (22.72±2.46 °C) are higher than in donkeys (18.88±2.30 °C). This could be related to differences in the skin thickness and hair coat. The patterns of both species are associated with GORs, rather than with an individual ROI, and there is a higher uniformity in the donkeys’ patterns.
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