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. 2020 Nov 24;10(12):2201.
doi: 10.3390/ani10122201.

Comparison of the Surface Thermal Patterns of Horses and Donkeys in Infrared Thermography Images

Małgorzata Domino  1 , Michał Romaszewski  2 , Tomasz Jasiński  1 , Małgorzata Maśko  3
Affiliations

Comparison of the Surface Thermal Patterns of Horses and Donkeys in Infrared Thermography Images

Małgorzata Domino et al. Animals (Basel). .

Abstract

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.

Keywords: equids; hair coat; infrared thermography; skin thickness; surface temperature; thermal patterns.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure A1
Figure A1
Histograms of temperatures for every ROI. The red color denotes horses, and the blue color denotes donkeys. The last plot presents the combined histogram for all ROIs.
Figure A2
Figure A2
Thermal patterns, i.e., statistically significant (p < 0.001) differences between the mean temperatures in GORs for the two donkeys identified as outliers (see Section 2.1): (a) donkey D.17; (b) donkey D.18. Bold font indicates the statistical significance of the difference for the given pattern (p < 0.001).
Figure 1
Figure 1
Example of an ultrasonographic image taken over the third lumbar vertebra: (a) the horse H.1; (b) the donkey D.3. The subcutaneous fat plus skin thickness (SF-Skin) is highlighted.
Figure 2
Figure 2
Visualization of a donkey D.3: (a) thermal data from the camera as a thermal map; (b) annotated classes corresponding to selected ROIs (see Section 2.2.1); (c) extracted pixels used in the experiments
Figure 3
Figure 3
Visualization of a donkey D.3, divided into groups of ROIs (GORs).
Figure 4
Figure 4
Visualization of temperatures in ROIs (ordered by their medians) of all animals: (a) horses; (b) donkeys.
Figure 5
Figure 5
Histograms of temperatures for two ROIs where the difference ∆t between mean values of temperatures for the two animal species is: (a) the smallest (ROI 4, ∆t = 1.59) and (b) the largest (ROI 7, ∆t = 5.26).
Figure 6
Figure 6
. t-SNE visualization of the dataset. Every dot represents an animal described with features extracted from the pixels of its 15 ROIs. Plots present different feature extraction statistics: (a) the mean; (b) the standard deviation; (c) the kurtosis; (d) the mean, after removing the global mean temperature of an animal from all pixel values. Notice that the examples in Plot (a) form clusters that correspond to the species of the animal, although some examples are in the wrong cluster.
Figure 7
Figure 7
Thermal maps of annotated ROIs for horses in our dataset.
Figure 8
Figure 8
Thermal maps of annotated ROIs for donkeys in our dataset.
Figure 9
Figure 9
Comparison of temperature histograms between animal species in identified characteristic areas corresponding to selected groups of ROIs: (a,) GOR 2 Front quarter; (b) GOR 4 Hindquarter; (c) GOR 5 Rump; (d) GOR 9 Groins. Horses are represented in red and donkeys in blue.
Figure 10
Figure 10
Selected examples of two animals from the dataset. The color map values tc for images in the upper row are scaled to the common range, which makes them easy to compare: (a) horses; (b) donkeys. Images in the bottom row are scaled to the minimal and maximal temperatures in the annotated ROIs of each animal, which highlights individual thermal patterns: (c) horses; (d) donkeys; e.g., warm horse’s GORs Abdomen and Neck, cool donkey’s GOR Rump, and warm donkey’s GOR Front quarter.
Figure 11
Figure 11
Thermal patterns and statistically significant differences between GORs. The upper panels present the matrix of differences within one species: (a) horses; (b) donkeys; e.g., the value M[4,0]Δ = −2.12 in the cell [4, 0] in Panel (a) is the difference between the mean temperatures for the pair Rump and Neck, indicating that the Rump GOR is colder. Bold font indicates “global” statistical significance of this difference (p < 0.001). The bottom panels present tables for (c) horses and (d) donkeys, with the number of animals for which the corresponding temperature difference in the table above is statistically significant considering individual thermal pattern of this animal (p < 0.001); e.g., the value M[4,0]L = 15 in Panel (c), which indicates that the pattern Rump and Neck is locally significant for 15 horses. A stable pattern should be statistically significant simultaneously for all data combined and for each of the 16 animals of a given species.
Figure 12
Figure 12
Comparison of thermal patterns for both species: (a) division of thermal patterns into six classes: SPS denotes thermal patterns that are similar and globally statistically significant (p < 0.001) for both species; SP: similar patterns, but not significant; HWS: opposite patterns where horses are warmer (and donkeys colder), which are statistically significant; HW: same as HWS, but not significant; HCS: significant patterns where horses are colder (and donkeys warmer); HC: same as HCS, but not significant. Note that the SPS class is the most common, which suggests the global similarity of patterns. (b) The minimum number of animals that locally confirm the global trend for classes SPS, HWS, and HCS, i.e., for both species, at least this number of animals share a given pattern individually (p < 0.001). Note that the maximum value in the table is 16, which indicates a stable pattern.
Figure 13
Figure 13
Differences between donkeys D.17 and D.18, outlier cases, and the rest of the donkeys, i.e., animals D.1-16. The upper panels present thermal maps of the two cases: (a) donkey D.17; (b) donkey D.18. Donkey D.17 was colder than other animals due to its long hair length. Donkey D.18 had an unusual pattern of warm areas resulting from patchy hair loss. Bottom plots show differences in the thermal patterns of these donkeys compared to the global pattern of other donkeys: (c) donkey D.17 compared to other donkeys; (d) donkey D.18 compared to other donkeys. The S class (green) indicates that the individual animal pattern was in line with the global trend, and class NS (red) indicates the opposite.
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