A novel approach to thermographic images analysis of equine thoracolumbar region: the effect of effort and rider’s body weight on structural image complexity

Abstract

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.

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

“…SumEntrp and Entropy increased with the progression of pregnancy in both the Red and I-components. Recent research using the novel approach of thermal image analysis of the equine thoracolumbar region showed that both Entropy and DifEntrp were informative variables in differentiating between two physiological conditions: working under a heavy or a light rider [ 31 ]. The change in physiological condition was suggested to increase the degree of thermal energy dissipation detectable as an increased entropy measure.…”

“…Moreover, in medical science, DIP increased the sensitivity and specificity of non-invasive diagnostic imaging, in the diagnosis of diabetes [ 25 ], skin cancer [ 26 ], and breast tumors [ 27 ]. However, the application of DIP to thermal images in equine medicine is only in the early stages of development [ 18 , 31 ]. In this study, two aspects of DIP, transformation to color models and image texture extraction, have been applied using IRT to enable earlier and more accurate detection of pregnancy in mares.…”

“…GLCM and Grey-Level Run-Length Matrix (GRLM), are both detailed approaches of Gray-Level Matrices (GLM). The GLM has been recently applied in equine medicine to detect the influence of a rider’s body weight on the thermography of the thoracolumbar region [ 31 ]. Moreover, GLM has successfully been used in human medicine to improve the extraction of texture features of ultrasound images [ 30 ], radiographic images [ 42 ], magnetic resonance images [ 29 , 43 ], and thermal images [ 27 , 44 , 45 , 46 , 47 , 48 ].…”

The Effect of Rider:Horse Bodyweight Ratio on the Superficial Body Temperature of Horse’s Thoracolumbar Region Evaluated by Advanced Thermal Image Processing

“…Therefore, in the case of horses used for riding, other more specific indicators of equine well-being are required to assess rider:horse interaction. Recently, the infrared thermography (IRT) measurement of the selected regions of the body surface temperature was proposed to assess both the horse’s emotional state [ 18 , 19 , 20 ] and the horse’s physiological response to an effort [ 17 , 21 , 22 , 23 ], also including the evaluation of the impact of the rider:horse bodyweight ratio on the equine organism [ 24 , 25 ].…”

“…It was also shown that of two riders representing 10.6% and 21.3% rider:horse bodyweight ratio, the horses ridden by the heavier rider demonstrated increased heart rate and superficial body temperature on the horse’s neck and trunk [ 24 ]. However, the effect of riders representing 10.1% and 15.3% rider:horse bodyweight ratio demonstrated no significant difference with respect to five conventional superficial body temperature measurement approaches in contrast with thermal image analysis based on the gray-level matrices (GLM) [ 25 ]. As Dyson et al [ 30 ] highlight, following the World Horse Welfare and the British Equestrian Federation, ‘innovative ways should be developed, so that riders can assess if they are the correct weight for their horse’, advanced digital image processing (DIP) has been introduced here to explore the effect of rider:horse bodyweight ratio on thermal images, which may be integrated into everyday equine practice.…”