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Establishing the thermal patterns of healthy people from Medellín, Colombia
Infrared Physics and Technology 95 (2018) 203–212
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Regular article
Establishing the thermal patterns of healthy people from Medellín, Colombia☆
T
María Camila Henao-Higuitaa,b, Alexandra Benítez-Mesaa,b, Hermes Fandiño-Torob, ⁎ Adriana Guerrero-Peñac, Gloria Díaz-Londoñoa, a
Grupo de Investigación e Innovación Biomédica, Instituto Tecnológico Metropolitano, Calle 73 No. 76A-354, Vía al Volador, Medellín, Colombia Grupo de Automática, Electrónica y Ciencias Computacionales, Instituto Tecnológico Metropolitano, Carrera 31 No. 54-22, Medellín, Colombia c Grupo de Investigación Didactica y Modelamiento en Ciencias DaVinci, Instituto Tecnológico Metropolitano, Carrera 31 No. 54-22, Medellín, Colombia b
ARTICLE INFO
ABSTRACT
Keywords: Infrared thermography Thermal patterns Healthy people
Infrared Thermography has been used in the medical field to diagnose illnesses that produce temperature variations. Furthermore, comparisons between the temperatures of contralateral regions or patients and healthy people are used as thermography diagnostic criteria. Under both approaches, the difference in temperature is calculated and, if it falls outside normal ranges, it is considered a sign and symptom of illness. In previous works, the thermal patterns of healthy individuals have been determined in populations from Finland, Portugal, Taiwan, Brazil, and Mexico. However, a comparison of such patterns reveals meaningful discrepancies since the emissivity of the human skin depends on ethnic characteristics and age. Thus, it is necessary to estimate thermal patterns in healthy people from each population and calculate the maximum and minimum temperature differences in their contralateral regions. Specifically, the thermal patterns of healthy Colombian people have not been established so far. Therefore, this work aims to define the thermal patterns of the population of Medellín-Colombia. Such patterns can later be used as reference values to diagnose illnesses in different hospitals in the city. Thirty-seven healthy individuals participated in this study. Data was acquired from the anterior, posterior, right, and left side of the body using a FLIR A655SC thermal camera, and the acquisition protocol was defined in accordance with the method of previous work. The camera recorded twenty-four thermograms from each subject, which were segmented by region-growing and a grid mask algorithm, thus obtaining 43 regions of interest (ROI). The mean temperature and standard deviations of each ROI were also calculated. In most regions, the mean temperature varied between 31.16 °C and 34.58 °C. The lowest mean temperature and highest variability were found in acral regions. Independent samples were assumed in the statistical analysis, and the normality of the temperature of each ROI was verified by the Shapiro–Wilk test. Moreover, Student’s t-test and Mann–Whitney U test were used in the comparison of temperatures of contralateral regions with normal and non normal distributions, respectively. Finally, the temperature difference was found to be insignificant.
1. Introduction Objects with temperatures above absolute zero emit wavelengths in the electromagnetic spectrum called infrared radiation [1]. Infrared Thermography (IRT) allows to acquire 2D images of such radiation from a body, which can be used to detect the temperature distribution of objects in real time [2–8]. This is an advantage in the medical field because changes in body temperature allow to identify physiological variations that are associated with various pathologies. In addition, IRT does not induce any risk to human health, it is non-invasive, low-cost, and portable. For instance, the joints of knees, ankles, fingers, and elbows of patients
with acute rheumatoid arthritis and osteoarthritis show high temperatures due to inflammation and pain. Enthesiopathies, fibromyalgia, tennis elbow, and muscle spasm and injury also present hot spots and swelling in different muscles [1,3,4,6,9]. Similarly, the eyes of patients with dry eye syndrome, Graves ophthalmopathy, and ocular diseases exhibit temperatures higher than those of healthy people [1,10–13]. Additionally, the temperature of the eyes can be used for fever screening [1,3,9]. Areas with low temperature (caused by cold stimulation, muscle contraction, and reduced motion) can appear in cases of Raynaud’s phenomenon, carpal tunnel syndrome, thoracic outlet syndrome, long lasting injuries, osteoarthritis of the hip, and frozen shoulder [3,14–17]. Furthermore, the
Fully documented templates are available in the elsarticle package on CTAN. Corresponding author. E-mail addresses: [email protected] (M.C. Henao-Higuita), [email protected] (A. Benítez-Mesa), [email protected] (H. Fandiño-Toro), [email protected] (A. Guerrero-Peña), [email protected] (G. Díaz-Londoño). ☆ ⁎
https://doi.org/10.1016/j.infrared.2018.10.038 Received 28 February 2018; Received in revised form 17 September 2018; Accepted 30 October 2018 Available online 31 October 2018 1350-4495/ © 2018 Elsevier B.V. All rights reserved.
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In the above-mentioned cases, temperature differences between contralateral regions or between healthy people and patients are calculated and used as diagnostic criteria. Furthermore, if the temperature differences are significant, they are considered a sign and symptom of a disease. Otherwise, these differences fall within the temperature range of normal healthy people or measurement uncertainty. Nonetheless, the emissivity of the human skin changes according to ethnic characteristics [1,9,22,23] and age[9,22,23]. This indicates that the thermal patterns of healthy people are specific to each population and, therefore, they should be established independently. Moreover, maximum and minimum temperature differences between contralateral regions should be calculated for each community that exhibits comparable ethnic characteristics. Some previous studies have established thermal patterns in healthy Brazilian [9], Finnish [9,24], Portuguese [25], Taiwanese [26], and Mexican populations [22]. In the case of Colombia, no work about thermal patterns has been reported. However, as this is a multi-ethnic nation and the average humidity and temperature change from city to city, said patterns should be determined in each region of the country. In order to establish thermal patterns, the segmentation methods should be properly selected for each Region of interest (ROI) since they can affect the final thermal results [27]. Anterior and posterior regions of different limbs of the body have been considered in previous studies on thermal patterns of healthy people [9,24,26]. The images were segmented using the software of the thermal cameras, which allows to define circular, rectangular, and square ROIs; however, these procedures can include background pixels, other ROIs, or not consider pixels of the ROI under analysis. As a result, average and standard deviations can change due to these extreme data [28]. Other works have used Otsu’s method to segment thermal images [29–31]. For example, Barcelos et al. [31] analyzed thermal images of the lower limbs of soccer players. However, they combined Otsu’s method with a correction method because the former has been reported to find inaccurate thresholds in thermal images with large variances in object and background intensities; thus, erroneous segmentation results can be generated [31,32]. This study aims to establish thermal patterns of the population of Medellín, Colombia, because this information can be used to diagnose illnesses. Thermal images were acquired from the anterior, posterior, right, and left side of the entire body, and 24 thermograms were recorded. A total of 43 ROIs were defined in each subject, including the eyes, nose, ears, and mouth. Although these organs were not studied in previous works about the thermal patterns of healthy adults, they can be used to evaluate emotions, feelings, and stress [33–36]. The thermal images were segmented using the region-growing (RG) segmentation method [37] and a grid mask algorithm written in Matlab. These segmentation methods have not been used in previous works about the thermal patterns of healthy people. On the other hand, these methods allow to obtain an accurate definition of anatomical regions and provide thermal results with small standard deviations. The mean temperature (T ) and standard deviations (SD) of each ROI were calculated, and the normality in the ROIs was verified by the Shapiro–Wilk test. In addition, Student’s t-test and Mann–Whitney U test were applied in the comparison between contralateral regions with normal and non normal distributions, respectively. Independent samples were assumed in the statistical analysis, and IBM SPSS Statistics 24 software was used.
Fig. 1. Set-up to measure the temperature of healthy people.
Fig. 2. Region of interest analyzed in this work for Right (R) and Left (L) side. 1. Forehead. 2. R Eye, 3. L Eye, 4. Nose, 5. R Cheekbone, 6. L Cheekbone, 7. Mouth, 8. Chin, 9. Neck, 10. R Helix, 11. R Outer ear, 12. L Helix, 13. L Outer ear, 14. R Thorax and abdomen, 15. L Thorax and abdomen, 16. R Shoulder, 17. L Shoulder, 18. R Forearm, 19. L Forearm, 20. R Elbow, 21. L Elbow, 22. R Arm, 23. L Arm, 24. R Wrist, 25. L Wrist, 26. R Palm, 27. R Thumb, 28. R Index, 29. R Middle, 30. R Ring, 31. R Little or Pinky, 32. L Palm, 33. L Thumb, 34. L Index, 35. L Middle, 36. L Ring, 37. L Little or Pinky, 38. R Thigh, 39. L Thigh, 40. R Knee, 41. L Knee, 42. R Leg, 43. L Leg.
2. Materials and methods Thirty-seven healthy people participated in this study [average age: 27 ± 5 years, average height: 1.68 ± 0.07 m, average weight: 66.52 ± 9.19 kg, and average Body Mass Index (BMI): 23.43 ± 2.89 kg/ m2 ]. The group was composed of 17 females and 20 males who were residents of Medellín. The thermograms were acquired using a FLIR A655SC thermal camera with a long wavelength infrared band (7.5 to 14 µ m), a spatial resolution of 640 × 480 pixels, an accuracy of ± 2 °C, and a NETD under 30 mK. Said camera uses FLIR R&D software 3.3. A total of 24 thermograms were recorded from different views (anterior,
comparison of temperatures between the affected and non-affected region is one of the diagnostic criteria for diseases such as complex regional pain syndrome, localized scleroderma, breast cancer, and diabetes neuropathy [3,18]. In the case of localized scleroderma, a temperature difference over 0.5 °C between contralateral regions indicates lesion activity [19,20]. In sport and exercise medicine, the assessment of bilateral asymmetry assumes a relevance to determine potential risks of injury and functional deficits of unhealthy athletes and non-athletes [21]. 204
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Fig. 3. Region-growing segmentation of forehead and neck ROIs. (a) Example of a thermogram. (b) Superimposition of forehead and neck ROIs on the thermogram in (a).
Fig. 4. The grid mask algorithm applied onto the trunk. (a) Example of a thermogram. (b) Superimposition of the grid mask on the thermogram in (a).
posterior, right, and left side) of each person in standing position. During the measurements, men wore boxer shorts and women wore shorts (topless). As recommended by previous studies, the participants relaxed for 15 min before the measurements were taken. In addition, the subjects could not engage in physical activity or present pain in any part of the body 12 h prior to the measurements [1,3,24]. The subjects answered a survey that contained exclusion criteria such as any illness that could affect skin temperature, other types of alteration in the metabolism (the thyroid or the peripheral nervous system), or if they had suffered any bone fracture. The female survey included additional questions about hormonal cycle (menstrual period) and gynecological conditions and processes such as lactation, myomas, cysts, polycystic ovaries, and gestation, which served as exclusion criteria for women. Moreover, participants were instructed to avoid smoking, drinking alcohol, and energy and stimulating beverages in order to prevent thermal alterations; they were also informed that skin creams and lotions should not be applied on the skin [28]. All the subjects signed an informed consent approved by the Ethics Committee of Instituto Tecnológico Metropolitano (ITM).
July 2017. The room (4.36 × 5.20 m2 ) was adapted to prevent sunlight penetration. The mean distance between the subject and the camera was 2.15 ± 0.08 m. This distance (d) was calculated with Eq. (1), which determines the area (A) required to visualize images with the camera’s field-of-view (FOV). The latter is defined by and , which represent the vertical and horizontal aperture angles of the camera lens, respectively (FOV = × ). For the FLIR A655SC camera, these values are = 19° and = 25°. 1/2
d=
A
4 × tan
( ) × tan ( ) 2
(1)
2
Additionally, a black wood screen (2.00 × 1.20 ) was designed to frame each subject and provide a uniform background in the thermograms; this structure is lightweight and can be easily moved. Fig. 1 shows the geometric configuration inside the doctor’s office and the black wood screen.
m2
2.2. Acquisition protocol
2.1. Set-up to measure the temperature of healthy people
Most of the settings for the acquisition protocol were defined according to recommendations in previous works [1,3,24,38]. The ambient temperature was 25.63 ± 0.35 °C and the relative humidity, 48.76 ± 5.50 %. These
All the measurements were carried out in the doctor’s office at ITM in 205
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parameters were first measured at different times of the day in order to identify the period that presented the slightest fluctuations. As a result, data were collected in the morning, when the lowest level of fluctuations occurred [38]. Temperature and humidity were measured with a hygro-thermometer and recorded for each acquisition. Based on the results of the reflector method proposed by ASTM standard E1862 [39], in this case the value of reflected temperature was equal to the ambient temperature.
because the TH threshold guarantees that the segmented regions do not include pixels from the background of the thermograms. The function of the grid mask algorithm is to apply a grid that divides thermograms into square regions, which helps in the segmentation of comparatively large body regions, such as the trunk and thighs. Fig. 4 shows the segmentation of the posterior side of the trunk. 2.4. Statistical methods
2.3. Thermogram processing
In this work, the sample size was determined according to accuracy data, which depends on the margin of error (e) and a 95% confidence level. Furthermore, the following expression was used to calculate the optimum sample size [41–43]:
A series of codes written in Matlab were programmed for processing the acquired thermograms. Additionally, 43 ROIs of each subject were segmented (Fig. 2). The segmentation was carried out using the RG segmentation method [37] and a grid mask algorithm. RG segmentation starts with a pixel chosen as the seed and a TH value set as the threshold; based on them, neighboring pixels are added. Finally, the algorithm stops when the differences between the region’s mean and new pixels are higher than TH [40]. The forehead, eyes, noise, ears, joints, and phalanges were segmented with this algorithm. Fig. 3 shows the results of the RG segmentation of forehead and neck ROIs. For a better visualization, each ROI is framed by a black rectangle whose centroid (white dot) is the corresponding seed pixel. During the experiments, RG segmentation proved to be a suitable method
n=
Z12
/2 SD e2
2
(2)
where n is the sample size and Z1 /2 is the value of standard normal distribution for a 95% confidence level ( = 0.05). In this case, Z0.975 = 1.96. A pilot experiment with six subjects enabled to calculate the SD of the temperature(0.4435) and define e as a value lower than 1% of T of the trunk in the pilot experiment (0.145 °C). At this point, the sample size was 36 subjects.
Table 1 Mean temperature and standard deviations (T ± SD) in °C and p-values (p) of Shapiro–Wilk test in % of the right (R) and left (L) ROIs under analysis. View Region of interest
Anterior
T ± SD Forehead R Eye L Eye R Cheekbone L Cheekbone Nose Mouth Chin R Helix L Helix R Outer Ear L Outer Ear Neck R Thorax and Abdomen L Thorax and Abdomen R Shoulder L Shoulder R Arm L Arm R Elbow L Elbow R Forearm L Forearm R Wrist L Wrist R Palm or Back L Palm or Back R Thumb L Thumb R Index Finger L Index Finger R Middle Finger L Middle Finger R Ring Finger L Ring Finger R Pinky L Pinky R Thigh L Thigh R Knee L Knee R Leg L Leg
34.56 ± 34.32 ± 34.32 ± 33.80 ± 33.75 ± 33.71 ± 34.36 ± 34.23 ± – – – – 34.39 ± 33.29 ± 33.29 ± 33.54 ± 33.53 ± 32.94 ± 32.89 ± 32.72 ± 32.64 ± 32.81 ± 32.77 ± 32.99 ± 32.85 ± 32.58 ± 32.47 ± 32.20 ± 32.24 ± 31.83 ± 31.83 ± 31.98 ± 31.85 ± 31.63 ± 31.93 ± 31.23 ± 31.61 ± 32.27 ± 32.26 ± 31.39 ± 31.36 ± 32.12 ± 32.08 ±
0.49 0.37 0.41 0.62 0.64 1.11 0.38 0.55
0.52 0.82 0.83 0.71 0.69 0.68 0.70 0.62 0.69 0.64 0.66 0.75 0.75 1.31 1.27 1.47 1.38 1.94 1.75 1.75 1.78 1.98 1.55 2.34 1.87 0.67 0.69 0.72 0.76 0.63 0.68
Right side
Left side
p
T ± SD
p
7.20 5.10 25.00 27.70 63.10 4.20 15.10 53.30 – – – – 65.80 99.90 90.40 93.80 75.10 43.50 84.80 18.70 2.60 16.10 9.70 49.40 60.60 12.90 10.60 17.40 7.10 2.70 1.90 7.80 0.70 6.90 15.50 1.60 14.80 53.20 47.90 71.60 91.60 12.00 73.70
34.58 ± 0.46 34.29 ± 0.56 – 34.16 ± 0.73 – 32.99 ± 1.50 34.34 ± 0.53 34.18 ± 0.42 31.16 ± 0.67 – 34.64 ± 0.50 – 34.42 ± 0.50 32.97 ± 0.88 – – – – – – – 32.81 ± 0.62 32.72 ± 0.64 32.72 ± 0.69 32.69 ± 0.69 – – – – – – – – – – – – 31.99 ± 0.96 32.40 ± 0.75 31.78 ± 0.68 31.68 ± 0.69 32.08 ± 0.62 31.90 ± 0.71
7.00 52.50 – 8.40 – 0.20 56.00 13.30 99.40 – 45.40 – 60.50 84.40 – – – – – – – 49.90 12.70 44.20 45.20 – – – – – – – – – – – – 99.70 82.10 45.60 100.00 20.70 38.20
206
T ± SD 34.54 ± – 34.09 ± – 33.91 ± 33.54 ± 34.09 ± 34.14 ± – 31.69 ± – 34.54 ± 34.46 ± – 32.98 ± – – – – – – 32.55 ± 32.42 ± 32.05 ± 31.98 ± – – – – – – – – – – – – 32.38 ± 31.90 ± 31.75 ± 31.74 ± 31.93 ± 32.13 ±
0.57 0.47 0.70 0.94 0.37 0.46 0.70
0.48 0.58
0.87
0.53 0.59 0.93 0.86
0.65 0.89 0.64 0.71 0.67 0.69
Posterior p
5.40 – 78.10 – 27.40 4.20 87.40 39.30 – 73.50 – 0.47 20.80 – 65.00 – – – – – – 44.50 75.10 57.80 10.80 – – – – – – – – – – – – 67.60 82.40 96.20 87.20 47.40 11.30
T ± SD – – – – – – – – – – – – 34.04 ± 33.02 ± 33.09 ± 32.94 ± 32.76 ± 31.72 ± 31.60 ± 32.19 ± 32.02 ± 32.50 ± 32.47 ± 32.33 ± 32.18 ± 32.26 ± 32.15 ± 32.22 ± 32.10 ± 32.10 ± 32.25 ± 31.96 ± 32.46 ± 32.26 ± 32.17 ± 31.75 ± 31.26 ± 32.10 ± 32.16 ± 32.40 ± 32.44 ± 31.75 ± 31.81 ±
0.61 0.92 0.97 0.78 0.76 0.71 0.66 0.62 0.62 0.51 0.56 0.87 0.82 1.23 1.27 1.57 1.65 1.71 1.48 1.93 1.38 1.65 1.73 1.92 2.38 0.78 0.83 0.57 0.60 0.63 0.63
p – – – – – – – – – – – – 70.90 28.50 28.50 6.90 4.30 94.70 64.70 6.80 48.10 10.20 36.60 59.60 86.00 15.70 61.80 1.10 1.00 2.80 7.20 1.30 9.40 5.50 0.60 6.50 0.60 98.60 96.70 26.30 80.30 79.10 28.30
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In addition, the authors ensured that the temperature was normally distributed; therefore, a sample size above 30 individuals provided a good estimation of temperature (central limit theorem) [41–44]. Based on the considerations above, 37 healthy people were studied. The statistic sampling technique used simple random sampling with a confidence level of 0.95 and a sampling error of 0.145 °C; in addition, independent samples were assumed. The software IBM SPSS Statistics 24 was used to analyze the results. T and SD were calculated for each ROI and the Shapiro–Wilk test [44] was applied to evaluate the normality of the data. Parametric and non-parametric tests were carried out on contralateral ROIs in order to define temperature differences between the latter. If the ROIs exhibited a normal distribution, Levene’s test and Student’s t-test were used to assess the equality of variances and T , respectively. If the ROIs did not show a normal distribution, Mann–Whitney U test [45] was applied to estimate the equality of T . In all the tests, the significance level was = 0.05.
regions under study. In most regions, T varied between 31.16 °C and 34.58 °C, but in the external outer ear, forehead, and neck it was over 34.54 °C. In the helix, knee, anterior view of the right pinky finger, posterior view of the left pinky finger, and left arm, it ranged between 31.16 °C and 31.60 °C. The SDs of all the ROIs were between 0.36 °C and 2.34 °C. The highest values were found on the nose, hands, and phalanges because these ROIs are acral regions of the body, they are less irrigated by blood than central areas, and such irrigation presents higher variability. Most regions exhibited a normal distribution because their p-value was over 5 % , except for the nose, helix, left shoulder, left elbow, and fingers. 3.2. Analysis of contralateral regions Table 2 shows the results of T and SD of the entire regions, as well as the parametric and non-parametric tests of the comparison between contralateral ROIs. In addition, the temperature differences between left and right regions were calculated, TRL . The variances of most contralateral ROIs show no significant differences. Furthermore, Student’s t-test for equality variance was applied and the temperature difference was found to be insignificant. Nonetheless, a significant difference between variances was found in the eyes. Student’s t-test for unequal variance was applied and the p-values were
3. Results and discussion 3.1. Average and standard deviations Table 1 shows the results of T , SD, and the p-value normality test (pvalue) [44] of the anterior, posterior, right, and left lateral views of the
Table 2 Mean temperature and standard deviations (T ± SD) in °C and p-values (p) of Student’s t test and Mann–whitney U test in % of the comparison between contralateral regions, right (R) and left (L). Parametric and Non-parametric test Region of interest
Student’s t-test
T ± SD Forehead R Eye L Eye R Cheekbone L Cheekbone Nose Mouth Chin R Helix L Helix R Outer Ear L Outer Ear Neck R Thorax and Abdomen L Thorax and Abdomen R Shoulder L Shoulder R Arm L Arm R Elbow L Elbow R Forearm L Forearm R Wrist L Wrist R Palm and Back L Palm and Back R Thumb L Thumb R Index Finger L Index Finger R Middle Finger L Middle Finger R Ring Finger L Ring Finger R Pinky L Pinky R Thigh L Thigh R Knee L Knee R Leg L Leg
34.557 ± 34.308 ± 34.206 ± 33.969 ± 33.846 ± 33.311 ± 34.299 ± 34.193 ± 31.392 ± 31.256 ± 34.566 ± 34.572 ± 34.278 ± 33.154 ± 33.192 ± 33.179 ± 33.117 ± 32.273 ± 32.170 ± 32.405 ± 32.289 ± 32.674 ± 32.565 ± 32.342 ± 32.222 ± 32.417 ± 32.304 ± 32.211 ± 32.186 ± 31.887 ± 32.056 ± 31.966 ± 32.102 ± 31.934 ± 32.050 ± 31.481 ± 31.425 ± 32.182 ± 32.184 ± 31.828 ± 31.806 ± 31.966 ± 31.979 ±
0.512 0.473 0.432 0.675 0.663 1.454 0.418 0.493 0.866 0.741 0.494 0.459 0.615 0.875 0.886 0.837 0.847 0.971 0.963 0.728 0.751 0.619 0.628 1.022 1.019 1.273 1.273 1.509 1.476 1.908 1.590 1.827 1.622 1.839 1.633 2.149 2.142 0.782 0.802 0.744 0.790 0.647 0.683
Mann–Whitney U test
TRL
p
p
– 0.102 ± 0.206 – 0.123 ± 0.305 – – – – 0.136 ± 0.367 – 0.006 ± 0.217 – – 0.038 ± 0.401 – 0.062 ± 0.384 – 0.103 ± 0.441 – 0.116 ± 0.337 – 0.109 ± 0.284 – 0.120 ± 0.465 – 0.113 ± 0.580 – 0.025 ± 0.680 – 0.169 ± 0.800 – 0.136 ± 0.787 – 0.116 ± 0.792 – 0.056 ± 0.978 – 0.002 ± 0.361 – 0.022 ± 0.350 – 0.013 ± 0.361 –
98.900 10.800 – 16.600 – – 22.700 49.000 53.300 – 99.000 – 3.400 75.800 – 65.800 – 52.500 – 34.600 – 15.400 – 36.200 – – – – – – – – – – – – – 98.400 – 80.900 – 86.700 –
– – – – – 10.900 – – – – – – – – – – – – – – – – – – – 59.100 – 90.300 – 79.700 – 85.800 – 82.300 – 87.200 – – – – – – –
207
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Table 3 Mean temperature and standard deviation (T ± SD) in °C of the right (R) and left (L) ROIs of the populations of Medellín, Brazil, and Taiwan. The control group is composed of females and males from Medellín, and the young group is comprised of Taiwanese people aged between 20 and 60.
Region of interest
Anterior
Posterior
Medellín
Brazil
Medellín
Brazil
Medellín
Taiwan
Female (n = 17)
Female (n = 117)
Male (n = 20)
Male (n = 103)
Control (n = 37)
Young (n = 37)
T ± SD
T ± SD
T ± SD
T ± SD
T ± SD
R Arm L Arm R Forearm L Forearm R Thigh L Thigh R Leg L Leg R Palm L Palm
32.66 32.63 32.56 32.54 32.07 32.04 32.00 31.96
R arm L Arm R Forearm L Forearm R Thigh L Thigh R Leg L Leg R Dorsal Hand L Dorsal Hand
31.35 ± 0.81 31.30 ± 0.79 32.45 ± 0, 69 32.35 ± 0.77 31.62 ± 0.90 31.71 ± 0.68 – – – –
± ± ± ± ± ± ± ± – –
0.86 0.88 0.77 0.89 0.47 0.62 0.56 0.52
28.40 28.60 30.39 30.59 28.62 28.72 30.18 30.11
± ± ± ± ± ± ± ± – –
33.07 ± 32.99 ± 32.67 ± 32.85 ± 32.46 ± 32.48 ± 32.21 ± 32.18 ± – –
2.20 2.20 1.20 1.20 1.30 1.30 1.20 1.20
0.63 0.67 0.50 0.62 0.78 0.71 0.69 0.79
31.95 ± 0.64 31.63 ± 0.53 32.67 ± 0.50 32.46 ± 0.46 32, 60 ± 0.71 32.52 ± 0.66 – – – –
28.59 ± 1.40 28.48 ± 1.40 30.20 ± 1, 20 29.95 ± 1.10 29.26 ± 1.10 29.15 ± 1.10 – – – –
over 5%, which indicates no significant difference in the T of the eyes. Moreover, significant differences between T were found in the neck, because the blood irrigation in lateral regions is higher than in anterior-posterior areas. Mann–Whitney U test was used to evaluate the equality of T in the regions that did not show a normal distribution, and no significant differences between T were found.
28.17 ± 2.61 28.34 ± 2.62 30.75 ± 1.20 30.96 ± 1.10 29.70 ± 1.10 29.75 ± 1.20 30.21 ± 1.40 30.19 ± 1.40 – –
– – 32.81 ± 32.77 ± 32.27 ± 32.26 ± 32.12 ± 32.08 ± 32.58 ± 32.47 ±
29.36 29.22 30.61 30.30 30.28 30.20
– – 32.50 ± 32.47 ± 32.10 ± 32.16 ± 31.75 ± 31.81 ± 32.26 ± 32.15 ±
± ± ± ± ± ± – – – –
1.20 1.20 1.10 1.10 1.10 1.20
T ± SD
0.64 0.66 0.67 0.69 0.63 0.68 1.31 1.27
– – 31.70 ± 31.60 ± 32.20 ± 32.20 ± 30.30 ± 30.40 ± 31.80 ± 31.40 ±
0.51 0.56 0.78 0.83 0.63 0.63 1.23 1.27
– – 31, 00 ± 0.60 30.50 ± 0.60 30.30 ± 0.60 30.30 ± 0.70 29.90 ± 0.70 29.90 ± 0.70 30.80 ± 0.70 30.50 ± 0.60
0.50 0.60 0.60 0.60 0.80 0.60 1.10 1.30
these aspects. The collected images enabled to determine the temperature value of 43 regions located throughout the body. Besides, statistical tests allowed to establish that the contralateral areas present a normal distribution, equal variance, and no significant differences, which makes such regions useful for the medical diagnosis of pathologies that affect thermal patterns. In order to correctly evaluate anatomically complex regions, such as the hands or ears, their sections should be considered separately (e.g., phalanges, back of hand, auricle of the external auditory canal) due to their variable thermal behavior. RG segmentation and the grid mask algorithm were essential for this study because they allowed to obtain, in a quick and effective way, data from the areas under analysis. With the aim of properly and more precisely selecting ROIs, future studies should implement software or programming algorithms to automatically segment the image, as this would enable to acquire more accurate thermal data. The method implemented in this study should be replicated in future works in other areas of Colombia where different ethnic characteristics are found in order to obtain the thermal patterns of each region. Finally, to determine a thermal difference that indicates a pathological behavior, it is necessary to establish a significant difference in each ROI using both parametric and non-parametric tests, which can be further developed in future works.
3.3. Comparison with previous works The results obtained in this work were compared with those reported by Marins et al. [9] and Niu et al. [26], who determined thermal patterns of upper and lower limbs of healthy Brazilian and Taiwanese people, respectively. Their methods and environmental conditions are similar to those described in this study. In this work, the temperature of the limbs varied between 31.30 °C and 33.07 °C; in Brazil, between 28.17 °C and 30.61 °C [9]; and in Taiwan, between 30.30 °C and 32.77 °C [26]. Therefore, the temperature range of the population of Medellín is higher than that of individuals in Brazil and Taiwan. Student’s t-test was applied to the comparison of temperatures among young adults in the three cases: Medellín, Brazil, and Taiwan. Table 3 shows the T and SD of this comparison. Significant differences between populations were found due to the fact that the p-values were below 5%. Such differences can be explained by the skin type of each population and atmospheric conditions. These results reveal the need to establish thermal patterns for each population.
Conflict of interest The authors declared that there is no conflict of interest.
4. Conclusions and recommendations
Acknowledgments
Conditions such as the technical characteristics of the camera and environmental and individual factors were essential to define the acquisition protocol, since the quality of the images that were obtained depended on
This work was supported by Instituto Tecnológico Metropolitano (ITM). Additionally, the authors would like to thank the medical service at ITM and the participants in this study.
Appendix A. Region of interest This Appendix contains the thermograms and ROIs under analysis. See Figs. A.1–A.4.
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Fig. A.1. Region-growing and grid mask algorithm segmentation of two ROIs: leg and thigh.
Fig. A.2. Region-growing segmentation of the ROIs of the arm. 209
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Fig. A.3. Region-growing segmentation of the ROIs of the hand.
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Fig. A.4. Region-growing segmentation of the ROIs of the head.
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Regular article
Establishing the thermal patterns of healthy people from Medellín, Colombia☆
T
María Camila Henao-Higuitaa,b, Alexandra Benítez-Mesaa,b, Hermes Fandiño-Torob, ⁎ Adriana Guerrero-Peñac, Gloria Díaz-Londoñoa, a
Grupo de Investigación e Innovación Biomédica, Instituto Tecnológico Metropolitano, Calle 73 No. 76A-354, Vía al Volador, Medellín, Colombia Grupo de Automática, Electrónica y Ciencias Computacionales, Instituto Tecnológico Metropolitano, Carrera 31 No. 54-22, Medellín, Colombia c Grupo de Investigación Didactica y Modelamiento en Ciencias DaVinci, Instituto Tecnológico Metropolitano, Carrera 31 No. 54-22, Medellín, Colombia b
ARTICLE INFO
ABSTRACT
Keywords: Infrared thermography Thermal patterns Healthy people
Infrared Thermography has been used in the medical field to diagnose illnesses that produce temperature variations. Furthermore, comparisons between the temperatures of contralateral regions or patients and healthy people are used as thermography diagnostic criteria. Under both approaches, the difference in temperature is calculated and, if it falls outside normal ranges, it is considered a sign and symptom of illness. In previous works, the thermal patterns of healthy individuals have been determined in populations from Finland, Portugal, Taiwan, Brazil, and Mexico. However, a comparison of such patterns reveals meaningful discrepancies since the emissivity of the human skin depends on ethnic characteristics and age. Thus, it is necessary to estimate thermal patterns in healthy people from each population and calculate the maximum and minimum temperature differences in their contralateral regions. Specifically, the thermal patterns of healthy Colombian people have not been established so far. Therefore, this work aims to define the thermal patterns of the population of Medellín-Colombia. Such patterns can later be used as reference values to diagnose illnesses in different hospitals in the city. Thirty-seven healthy individuals participated in this study. Data was acquired from the anterior, posterior, right, and left side of the body using a FLIR A655SC thermal camera, and the acquisition protocol was defined in accordance with the method of previous work. The camera recorded twenty-four thermograms from each subject, which were segmented by region-growing and a grid mask algorithm, thus obtaining 43 regions of interest (ROI). The mean temperature and standard deviations of each ROI were also calculated. In most regions, the mean temperature varied between 31.16 °C and 34.58 °C. The lowest mean temperature and highest variability were found in acral regions. Independent samples were assumed in the statistical analysis, and the normality of the temperature of each ROI was verified by the Shapiro–Wilk test. Moreover, Student’s t-test and Mann–Whitney U test were used in the comparison of temperatures of contralateral regions with normal and non normal distributions, respectively. Finally, the temperature difference was found to be insignificant.
1. Introduction Objects with temperatures above absolute zero emit wavelengths in the electromagnetic spectrum called infrared radiation [1]. Infrared Thermography (IRT) allows to acquire 2D images of such radiation from a body, which can be used to detect the temperature distribution of objects in real time [2–8]. This is an advantage in the medical field because changes in body temperature allow to identify physiological variations that are associated with various pathologies. In addition, IRT does not induce any risk to human health, it is non-invasive, low-cost, and portable. For instance, the joints of knees, ankles, fingers, and elbows of patients
with acute rheumatoid arthritis and osteoarthritis show high temperatures due to inflammation and pain. Enthesiopathies, fibromyalgia, tennis elbow, and muscle spasm and injury also present hot spots and swelling in different muscles [1,3,4,6,9]. Similarly, the eyes of patients with dry eye syndrome, Graves ophthalmopathy, and ocular diseases exhibit temperatures higher than those of healthy people [1,10–13]. Additionally, the temperature of the eyes can be used for fever screening [1,3,9]. Areas with low temperature (caused by cold stimulation, muscle contraction, and reduced motion) can appear in cases of Raynaud’s phenomenon, carpal tunnel syndrome, thoracic outlet syndrome, long lasting injuries, osteoarthritis of the hip, and frozen shoulder [3,14–17]. Furthermore, the
Fully documented templates are available in the elsarticle package on CTAN. Corresponding author. E-mail addresses: [email protected] (M.C. Henao-Higuita), [email protected] (A. Benítez-Mesa), [email protected] (H. Fandiño-Toro), [email protected] (A. Guerrero-Peña), [email protected] (G. Díaz-Londoño). ☆ ⁎
https://doi.org/10.1016/j.infrared.2018.10.038 Received 28 February 2018; Received in revised form 17 September 2018; Accepted 30 October 2018 Available online 31 October 2018 1350-4495/ © 2018 Elsevier B.V. All rights reserved.
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In the above-mentioned cases, temperature differences between contralateral regions or between healthy people and patients are calculated and used as diagnostic criteria. Furthermore, if the temperature differences are significant, they are considered a sign and symptom of a disease. Otherwise, these differences fall within the temperature range of normal healthy people or measurement uncertainty. Nonetheless, the emissivity of the human skin changes according to ethnic characteristics [1,9,22,23] and age[9,22,23]. This indicates that the thermal patterns of healthy people are specific to each population and, therefore, they should be established independently. Moreover, maximum and minimum temperature differences between contralateral regions should be calculated for each community that exhibits comparable ethnic characteristics. Some previous studies have established thermal patterns in healthy Brazilian [9], Finnish [9,24], Portuguese [25], Taiwanese [26], and Mexican populations [22]. In the case of Colombia, no work about thermal patterns has been reported. However, as this is a multi-ethnic nation and the average humidity and temperature change from city to city, said patterns should be determined in each region of the country. In order to establish thermal patterns, the segmentation methods should be properly selected for each Region of interest (ROI) since they can affect the final thermal results [27]. Anterior and posterior regions of different limbs of the body have been considered in previous studies on thermal patterns of healthy people [9,24,26]. The images were segmented using the software of the thermal cameras, which allows to define circular, rectangular, and square ROIs; however, these procedures can include background pixels, other ROIs, or not consider pixels of the ROI under analysis. As a result, average and standard deviations can change due to these extreme data [28]. Other works have used Otsu’s method to segment thermal images [29–31]. For example, Barcelos et al. [31] analyzed thermal images of the lower limbs of soccer players. However, they combined Otsu’s method with a correction method because the former has been reported to find inaccurate thresholds in thermal images with large variances in object and background intensities; thus, erroneous segmentation results can be generated [31,32]. This study aims to establish thermal patterns of the population of Medellín, Colombia, because this information can be used to diagnose illnesses. Thermal images were acquired from the anterior, posterior, right, and left side of the entire body, and 24 thermograms were recorded. A total of 43 ROIs were defined in each subject, including the eyes, nose, ears, and mouth. Although these organs were not studied in previous works about the thermal patterns of healthy adults, they can be used to evaluate emotions, feelings, and stress [33–36]. The thermal images were segmented using the region-growing (RG) segmentation method [37] and a grid mask algorithm written in Matlab. These segmentation methods have not been used in previous works about the thermal patterns of healthy people. On the other hand, these methods allow to obtain an accurate definition of anatomical regions and provide thermal results with small standard deviations. The mean temperature (T ) and standard deviations (SD) of each ROI were calculated, and the normality in the ROIs was verified by the Shapiro–Wilk test. In addition, Student’s t-test and Mann–Whitney U test were applied in the comparison between contralateral regions with normal and non normal distributions, respectively. Independent samples were assumed in the statistical analysis, and IBM SPSS Statistics 24 software was used.
Fig. 1. Set-up to measure the temperature of healthy people.
Fig. 2. Region of interest analyzed in this work for Right (R) and Left (L) side. 1. Forehead. 2. R Eye, 3. L Eye, 4. Nose, 5. R Cheekbone, 6. L Cheekbone, 7. Mouth, 8. Chin, 9. Neck, 10. R Helix, 11. R Outer ear, 12. L Helix, 13. L Outer ear, 14. R Thorax and abdomen, 15. L Thorax and abdomen, 16. R Shoulder, 17. L Shoulder, 18. R Forearm, 19. L Forearm, 20. R Elbow, 21. L Elbow, 22. R Arm, 23. L Arm, 24. R Wrist, 25. L Wrist, 26. R Palm, 27. R Thumb, 28. R Index, 29. R Middle, 30. R Ring, 31. R Little or Pinky, 32. L Palm, 33. L Thumb, 34. L Index, 35. L Middle, 36. L Ring, 37. L Little or Pinky, 38. R Thigh, 39. L Thigh, 40. R Knee, 41. L Knee, 42. R Leg, 43. L Leg.
2. Materials and methods Thirty-seven healthy people participated in this study [average age: 27 ± 5 years, average height: 1.68 ± 0.07 m, average weight: 66.52 ± 9.19 kg, and average Body Mass Index (BMI): 23.43 ± 2.89 kg/ m2 ]. The group was composed of 17 females and 20 males who were residents of Medellín. The thermograms were acquired using a FLIR A655SC thermal camera with a long wavelength infrared band (7.5 to 14 µ m), a spatial resolution of 640 × 480 pixels, an accuracy of ± 2 °C, and a NETD under 30 mK. Said camera uses FLIR R&D software 3.3. A total of 24 thermograms were recorded from different views (anterior,
comparison of temperatures between the affected and non-affected region is one of the diagnostic criteria for diseases such as complex regional pain syndrome, localized scleroderma, breast cancer, and diabetes neuropathy [3,18]. In the case of localized scleroderma, a temperature difference over 0.5 °C between contralateral regions indicates lesion activity [19,20]. In sport and exercise medicine, the assessment of bilateral asymmetry assumes a relevance to determine potential risks of injury and functional deficits of unhealthy athletes and non-athletes [21]. 204
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Fig. 3. Region-growing segmentation of forehead and neck ROIs. (a) Example of a thermogram. (b) Superimposition of forehead and neck ROIs on the thermogram in (a).
Fig. 4. The grid mask algorithm applied onto the trunk. (a) Example of a thermogram. (b) Superimposition of the grid mask on the thermogram in (a).
posterior, right, and left side) of each person in standing position. During the measurements, men wore boxer shorts and women wore shorts (topless). As recommended by previous studies, the participants relaxed for 15 min before the measurements were taken. In addition, the subjects could not engage in physical activity or present pain in any part of the body 12 h prior to the measurements [1,3,24]. The subjects answered a survey that contained exclusion criteria such as any illness that could affect skin temperature, other types of alteration in the metabolism (the thyroid or the peripheral nervous system), or if they had suffered any bone fracture. The female survey included additional questions about hormonal cycle (menstrual period) and gynecological conditions and processes such as lactation, myomas, cysts, polycystic ovaries, and gestation, which served as exclusion criteria for women. Moreover, participants were instructed to avoid smoking, drinking alcohol, and energy and stimulating beverages in order to prevent thermal alterations; they were also informed that skin creams and lotions should not be applied on the skin [28]. All the subjects signed an informed consent approved by the Ethics Committee of Instituto Tecnológico Metropolitano (ITM).
July 2017. The room (4.36 × 5.20 m2 ) was adapted to prevent sunlight penetration. The mean distance between the subject and the camera was 2.15 ± 0.08 m. This distance (d) was calculated with Eq. (1), which determines the area (A) required to visualize images with the camera’s field-of-view (FOV). The latter is defined by and , which represent the vertical and horizontal aperture angles of the camera lens, respectively (FOV = × ). For the FLIR A655SC camera, these values are = 19° and = 25°. 1/2
d=
A
4 × tan
( ) × tan ( ) 2
(1)
2
Additionally, a black wood screen (2.00 × 1.20 ) was designed to frame each subject and provide a uniform background in the thermograms; this structure is lightweight and can be easily moved. Fig. 1 shows the geometric configuration inside the doctor’s office and the black wood screen.
m2
2.2. Acquisition protocol
2.1. Set-up to measure the temperature of healthy people
Most of the settings for the acquisition protocol were defined according to recommendations in previous works [1,3,24,38]. The ambient temperature was 25.63 ± 0.35 °C and the relative humidity, 48.76 ± 5.50 %. These
All the measurements were carried out in the doctor’s office at ITM in 205
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parameters were first measured at different times of the day in order to identify the period that presented the slightest fluctuations. As a result, data were collected in the morning, when the lowest level of fluctuations occurred [38]. Temperature and humidity were measured with a hygro-thermometer and recorded for each acquisition. Based on the results of the reflector method proposed by ASTM standard E1862 [39], in this case the value of reflected temperature was equal to the ambient temperature.
because the TH threshold guarantees that the segmented regions do not include pixels from the background of the thermograms. The function of the grid mask algorithm is to apply a grid that divides thermograms into square regions, which helps in the segmentation of comparatively large body regions, such as the trunk and thighs. Fig. 4 shows the segmentation of the posterior side of the trunk. 2.4. Statistical methods
2.3. Thermogram processing
In this work, the sample size was determined according to accuracy data, which depends on the margin of error (e) and a 95% confidence level. Furthermore, the following expression was used to calculate the optimum sample size [41–43]:
A series of codes written in Matlab were programmed for processing the acquired thermograms. Additionally, 43 ROIs of each subject were segmented (Fig. 2). The segmentation was carried out using the RG segmentation method [37] and a grid mask algorithm. RG segmentation starts with a pixel chosen as the seed and a TH value set as the threshold; based on them, neighboring pixels are added. Finally, the algorithm stops when the differences between the region’s mean and new pixels are higher than TH [40]. The forehead, eyes, noise, ears, joints, and phalanges were segmented with this algorithm. Fig. 3 shows the results of the RG segmentation of forehead and neck ROIs. For a better visualization, each ROI is framed by a black rectangle whose centroid (white dot) is the corresponding seed pixel. During the experiments, RG segmentation proved to be a suitable method
n=
Z12
/2 SD e2
2
(2)
where n is the sample size and Z1 /2 is the value of standard normal distribution for a 95% confidence level ( = 0.05). In this case, Z0.975 = 1.96. A pilot experiment with six subjects enabled to calculate the SD of the temperature(0.4435) and define e as a value lower than 1% of T of the trunk in the pilot experiment (0.145 °C). At this point, the sample size was 36 subjects.
Table 1 Mean temperature and standard deviations (T ± SD) in °C and p-values (p) of Shapiro–Wilk test in % of the right (R) and left (L) ROIs under analysis. View Region of interest
Anterior
T ± SD Forehead R Eye L Eye R Cheekbone L Cheekbone Nose Mouth Chin R Helix L Helix R Outer Ear L Outer Ear Neck R Thorax and Abdomen L Thorax and Abdomen R Shoulder L Shoulder R Arm L Arm R Elbow L Elbow R Forearm L Forearm R Wrist L Wrist R Palm or Back L Palm or Back R Thumb L Thumb R Index Finger L Index Finger R Middle Finger L Middle Finger R Ring Finger L Ring Finger R Pinky L Pinky R Thigh L Thigh R Knee L Knee R Leg L Leg
34.56 ± 34.32 ± 34.32 ± 33.80 ± 33.75 ± 33.71 ± 34.36 ± 34.23 ± – – – – 34.39 ± 33.29 ± 33.29 ± 33.54 ± 33.53 ± 32.94 ± 32.89 ± 32.72 ± 32.64 ± 32.81 ± 32.77 ± 32.99 ± 32.85 ± 32.58 ± 32.47 ± 32.20 ± 32.24 ± 31.83 ± 31.83 ± 31.98 ± 31.85 ± 31.63 ± 31.93 ± 31.23 ± 31.61 ± 32.27 ± 32.26 ± 31.39 ± 31.36 ± 32.12 ± 32.08 ±
0.49 0.37 0.41 0.62 0.64 1.11 0.38 0.55
0.52 0.82 0.83 0.71 0.69 0.68 0.70 0.62 0.69 0.64 0.66 0.75 0.75 1.31 1.27 1.47 1.38 1.94 1.75 1.75 1.78 1.98 1.55 2.34 1.87 0.67 0.69 0.72 0.76 0.63 0.68
Right side
Left side
p
T ± SD
p
7.20 5.10 25.00 27.70 63.10 4.20 15.10 53.30 – – – – 65.80 99.90 90.40 93.80 75.10 43.50 84.80 18.70 2.60 16.10 9.70 49.40 60.60 12.90 10.60 17.40 7.10 2.70 1.90 7.80 0.70 6.90 15.50 1.60 14.80 53.20 47.90 71.60 91.60 12.00 73.70
34.58 ± 0.46 34.29 ± 0.56 – 34.16 ± 0.73 – 32.99 ± 1.50 34.34 ± 0.53 34.18 ± 0.42 31.16 ± 0.67 – 34.64 ± 0.50 – 34.42 ± 0.50 32.97 ± 0.88 – – – – – – – 32.81 ± 0.62 32.72 ± 0.64 32.72 ± 0.69 32.69 ± 0.69 – – – – – – – – – – – – 31.99 ± 0.96 32.40 ± 0.75 31.78 ± 0.68 31.68 ± 0.69 32.08 ± 0.62 31.90 ± 0.71
7.00 52.50 – 8.40 – 0.20 56.00 13.30 99.40 – 45.40 – 60.50 84.40 – – – – – – – 49.90 12.70 44.20 45.20 – – – – – – – – – – – – 99.70 82.10 45.60 100.00 20.70 38.20
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T ± SD 34.54 ± – 34.09 ± – 33.91 ± 33.54 ± 34.09 ± 34.14 ± – 31.69 ± – 34.54 ± 34.46 ± – 32.98 ± – – – – – – 32.55 ± 32.42 ± 32.05 ± 31.98 ± – – – – – – – – – – – – 32.38 ± 31.90 ± 31.75 ± 31.74 ± 31.93 ± 32.13 ±
0.57 0.47 0.70 0.94 0.37 0.46 0.70
0.48 0.58
0.87
0.53 0.59 0.93 0.86
0.65 0.89 0.64 0.71 0.67 0.69
Posterior p
5.40 – 78.10 – 27.40 4.20 87.40 39.30 – 73.50 – 0.47 20.80 – 65.00 – – – – – – 44.50 75.10 57.80 10.80 – – – – – – – – – – – – 67.60 82.40 96.20 87.20 47.40 11.30
T ± SD – – – – – – – – – – – – 34.04 ± 33.02 ± 33.09 ± 32.94 ± 32.76 ± 31.72 ± 31.60 ± 32.19 ± 32.02 ± 32.50 ± 32.47 ± 32.33 ± 32.18 ± 32.26 ± 32.15 ± 32.22 ± 32.10 ± 32.10 ± 32.25 ± 31.96 ± 32.46 ± 32.26 ± 32.17 ± 31.75 ± 31.26 ± 32.10 ± 32.16 ± 32.40 ± 32.44 ± 31.75 ± 31.81 ±
0.61 0.92 0.97 0.78 0.76 0.71 0.66 0.62 0.62 0.51 0.56 0.87 0.82 1.23 1.27 1.57 1.65 1.71 1.48 1.93 1.38 1.65 1.73 1.92 2.38 0.78 0.83 0.57 0.60 0.63 0.63
p – – – – – – – – – – – – 70.90 28.50 28.50 6.90 4.30 94.70 64.70 6.80 48.10 10.20 36.60 59.60 86.00 15.70 61.80 1.10 1.00 2.80 7.20 1.30 9.40 5.50 0.60 6.50 0.60 98.60 96.70 26.30 80.30 79.10 28.30
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In addition, the authors ensured that the temperature was normally distributed; therefore, a sample size above 30 individuals provided a good estimation of temperature (central limit theorem) [41–44]. Based on the considerations above, 37 healthy people were studied. The statistic sampling technique used simple random sampling with a confidence level of 0.95 and a sampling error of 0.145 °C; in addition, independent samples were assumed. The software IBM SPSS Statistics 24 was used to analyze the results. T and SD were calculated for each ROI and the Shapiro–Wilk test [44] was applied to evaluate the normality of the data. Parametric and non-parametric tests were carried out on contralateral ROIs in order to define temperature differences between the latter. If the ROIs exhibited a normal distribution, Levene’s test and Student’s t-test were used to assess the equality of variances and T , respectively. If the ROIs did not show a normal distribution, Mann–Whitney U test [45] was applied to estimate the equality of T . In all the tests, the significance level was = 0.05.
regions under study. In most regions, T varied between 31.16 °C and 34.58 °C, but in the external outer ear, forehead, and neck it was over 34.54 °C. In the helix, knee, anterior view of the right pinky finger, posterior view of the left pinky finger, and left arm, it ranged between 31.16 °C and 31.60 °C. The SDs of all the ROIs were between 0.36 °C and 2.34 °C. The highest values were found on the nose, hands, and phalanges because these ROIs are acral regions of the body, they are less irrigated by blood than central areas, and such irrigation presents higher variability. Most regions exhibited a normal distribution because their p-value was over 5 % , except for the nose, helix, left shoulder, left elbow, and fingers. 3.2. Analysis of contralateral regions Table 2 shows the results of T and SD of the entire regions, as well as the parametric and non-parametric tests of the comparison between contralateral ROIs. In addition, the temperature differences between left and right regions were calculated, TRL . The variances of most contralateral ROIs show no significant differences. Furthermore, Student’s t-test for equality variance was applied and the temperature difference was found to be insignificant. Nonetheless, a significant difference between variances was found in the eyes. Student’s t-test for unequal variance was applied and the p-values were
3. Results and discussion 3.1. Average and standard deviations Table 1 shows the results of T , SD, and the p-value normality test (pvalue) [44] of the anterior, posterior, right, and left lateral views of the
Table 2 Mean temperature and standard deviations (T ± SD) in °C and p-values (p) of Student’s t test and Mann–whitney U test in % of the comparison between contralateral regions, right (R) and left (L). Parametric and Non-parametric test Region of interest
Student’s t-test
T ± SD Forehead R Eye L Eye R Cheekbone L Cheekbone Nose Mouth Chin R Helix L Helix R Outer Ear L Outer Ear Neck R Thorax and Abdomen L Thorax and Abdomen R Shoulder L Shoulder R Arm L Arm R Elbow L Elbow R Forearm L Forearm R Wrist L Wrist R Palm and Back L Palm and Back R Thumb L Thumb R Index Finger L Index Finger R Middle Finger L Middle Finger R Ring Finger L Ring Finger R Pinky L Pinky R Thigh L Thigh R Knee L Knee R Leg L Leg
34.557 ± 34.308 ± 34.206 ± 33.969 ± 33.846 ± 33.311 ± 34.299 ± 34.193 ± 31.392 ± 31.256 ± 34.566 ± 34.572 ± 34.278 ± 33.154 ± 33.192 ± 33.179 ± 33.117 ± 32.273 ± 32.170 ± 32.405 ± 32.289 ± 32.674 ± 32.565 ± 32.342 ± 32.222 ± 32.417 ± 32.304 ± 32.211 ± 32.186 ± 31.887 ± 32.056 ± 31.966 ± 32.102 ± 31.934 ± 32.050 ± 31.481 ± 31.425 ± 32.182 ± 32.184 ± 31.828 ± 31.806 ± 31.966 ± 31.979 ±
0.512 0.473 0.432 0.675 0.663 1.454 0.418 0.493 0.866 0.741 0.494 0.459 0.615 0.875 0.886 0.837 0.847 0.971 0.963 0.728 0.751 0.619 0.628 1.022 1.019 1.273 1.273 1.509 1.476 1.908 1.590 1.827 1.622 1.839 1.633 2.149 2.142 0.782 0.802 0.744 0.790 0.647 0.683
Mann–Whitney U test
TRL
p
p
– 0.102 ± 0.206 – 0.123 ± 0.305 – – – – 0.136 ± 0.367 – 0.006 ± 0.217 – – 0.038 ± 0.401 – 0.062 ± 0.384 – 0.103 ± 0.441 – 0.116 ± 0.337 – 0.109 ± 0.284 – 0.120 ± 0.465 – 0.113 ± 0.580 – 0.025 ± 0.680 – 0.169 ± 0.800 – 0.136 ± 0.787 – 0.116 ± 0.792 – 0.056 ± 0.978 – 0.002 ± 0.361 – 0.022 ± 0.350 – 0.013 ± 0.361 –
98.900 10.800 – 16.600 – – 22.700 49.000 53.300 – 99.000 – 3.400 75.800 – 65.800 – 52.500 – 34.600 – 15.400 – 36.200 – – – – – – – – – – – – – 98.400 – 80.900 – 86.700 –
– – – – – 10.900 – – – – – – – – – – – – – – – – – – – 59.100 – 90.300 – 79.700 – 85.800 – 82.300 – 87.200 – – – – – – –
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Table 3 Mean temperature and standard deviation (T ± SD) in °C of the right (R) and left (L) ROIs of the populations of Medellín, Brazil, and Taiwan. The control group is composed of females and males from Medellín, and the young group is comprised of Taiwanese people aged between 20 and 60.
Region of interest
Anterior
Posterior
Medellín
Brazil
Medellín
Brazil
Medellín
Taiwan
Female (n = 17)
Female (n = 117)
Male (n = 20)
Male (n = 103)
Control (n = 37)
Young (n = 37)
T ± SD
T ± SD
T ± SD
T ± SD
T ± SD
R Arm L Arm R Forearm L Forearm R Thigh L Thigh R Leg L Leg R Palm L Palm
32.66 32.63 32.56 32.54 32.07 32.04 32.00 31.96
R arm L Arm R Forearm L Forearm R Thigh L Thigh R Leg L Leg R Dorsal Hand L Dorsal Hand
31.35 ± 0.81 31.30 ± 0.79 32.45 ± 0, 69 32.35 ± 0.77 31.62 ± 0.90 31.71 ± 0.68 – – – –
± ± ± ± ± ± ± ± – –
0.86 0.88 0.77 0.89 0.47 0.62 0.56 0.52
28.40 28.60 30.39 30.59 28.62 28.72 30.18 30.11
± ± ± ± ± ± ± ± – –
33.07 ± 32.99 ± 32.67 ± 32.85 ± 32.46 ± 32.48 ± 32.21 ± 32.18 ± – –
2.20 2.20 1.20 1.20 1.30 1.30 1.20 1.20
0.63 0.67 0.50 0.62 0.78 0.71 0.69 0.79
31.95 ± 0.64 31.63 ± 0.53 32.67 ± 0.50 32.46 ± 0.46 32, 60 ± 0.71 32.52 ± 0.66 – – – –
28.59 ± 1.40 28.48 ± 1.40 30.20 ± 1, 20 29.95 ± 1.10 29.26 ± 1.10 29.15 ± 1.10 – – – –
over 5%, which indicates no significant difference in the T of the eyes. Moreover, significant differences between T were found in the neck, because the blood irrigation in lateral regions is higher than in anterior-posterior areas. Mann–Whitney U test was used to evaluate the equality of T in the regions that did not show a normal distribution, and no significant differences between T were found.
28.17 ± 2.61 28.34 ± 2.62 30.75 ± 1.20 30.96 ± 1.10 29.70 ± 1.10 29.75 ± 1.20 30.21 ± 1.40 30.19 ± 1.40 – –
– – 32.81 ± 32.77 ± 32.27 ± 32.26 ± 32.12 ± 32.08 ± 32.58 ± 32.47 ±
29.36 29.22 30.61 30.30 30.28 30.20
– – 32.50 ± 32.47 ± 32.10 ± 32.16 ± 31.75 ± 31.81 ± 32.26 ± 32.15 ±
± ± ± ± ± ± – – – –
1.20 1.20 1.10 1.10 1.10 1.20
T ± SD
0.64 0.66 0.67 0.69 0.63 0.68 1.31 1.27
– – 31.70 ± 31.60 ± 32.20 ± 32.20 ± 30.30 ± 30.40 ± 31.80 ± 31.40 ±
0.51 0.56 0.78 0.83 0.63 0.63 1.23 1.27
– – 31, 00 ± 0.60 30.50 ± 0.60 30.30 ± 0.60 30.30 ± 0.70 29.90 ± 0.70 29.90 ± 0.70 30.80 ± 0.70 30.50 ± 0.60
0.50 0.60 0.60 0.60 0.80 0.60 1.10 1.30
these aspects. The collected images enabled to determine the temperature value of 43 regions located throughout the body. Besides, statistical tests allowed to establish that the contralateral areas present a normal distribution, equal variance, and no significant differences, which makes such regions useful for the medical diagnosis of pathologies that affect thermal patterns. In order to correctly evaluate anatomically complex regions, such as the hands or ears, their sections should be considered separately (e.g., phalanges, back of hand, auricle of the external auditory canal) due to their variable thermal behavior. RG segmentation and the grid mask algorithm were essential for this study because they allowed to obtain, in a quick and effective way, data from the areas under analysis. With the aim of properly and more precisely selecting ROIs, future studies should implement software or programming algorithms to automatically segment the image, as this would enable to acquire more accurate thermal data. The method implemented in this study should be replicated in future works in other areas of Colombia where different ethnic characteristics are found in order to obtain the thermal patterns of each region. Finally, to determine a thermal difference that indicates a pathological behavior, it is necessary to establish a significant difference in each ROI using both parametric and non-parametric tests, which can be further developed in future works.
3.3. Comparison with previous works The results obtained in this work were compared with those reported by Marins et al. [9] and Niu et al. [26], who determined thermal patterns of upper and lower limbs of healthy Brazilian and Taiwanese people, respectively. Their methods and environmental conditions are similar to those described in this study. In this work, the temperature of the limbs varied between 31.30 °C and 33.07 °C; in Brazil, between 28.17 °C and 30.61 °C [9]; and in Taiwan, between 30.30 °C and 32.77 °C [26]. Therefore, the temperature range of the population of Medellín is higher than that of individuals in Brazil and Taiwan. Student’s t-test was applied to the comparison of temperatures among young adults in the three cases: Medellín, Brazil, and Taiwan. Table 3 shows the T and SD of this comparison. Significant differences between populations were found due to the fact that the p-values were below 5%. Such differences can be explained by the skin type of each population and atmospheric conditions. These results reveal the need to establish thermal patterns for each population.
Conflict of interest The authors declared that there is no conflict of interest.
4. Conclusions and recommendations
Acknowledgments
Conditions such as the technical characteristics of the camera and environmental and individual factors were essential to define the acquisition protocol, since the quality of the images that were obtained depended on
This work was supported by Instituto Tecnológico Metropolitano (ITM). Additionally, the authors would like to thank the medical service at ITM and the participants in this study.
Appendix A. Region of interest This Appendix contains the thermograms and ROIs under analysis. See Figs. A.1–A.4.
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Fig. A.1. Region-growing and grid mask algorithm segmentation of two ROIs: leg and thigh.
Fig. A.2. Region-growing segmentation of the ROIs of the arm. 209
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Fig. A.3. Region-growing segmentation of the ROIs of the hand.
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Fig. A.4. Region-growing segmentation of the ROIs of the head.
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