Infrared thermography in the detection of brown adipose tissue in humans.

Jang C¹, Jalapu S², Thuzar M¹, Law PW³, Jeavons S⁴, Barclay JL⁵, Ho KK¹.

Author information

Abstract

PET-CT using ¹⁸F-FDG is employed for detecting brown adipose tissue (BAT) in humans. Alternative methods are needed because of the radiation and cost of PET-CT imaging. The aim was to evaluate the accuracy of infrared thermography (IRT) in detecting human BAT benchmarked to PET-CT imaging. Seventeen individuals underwent a total of 29 PET-CT scans, 12 of whom were studied twice, after 2 h of cold stimulation at 19°C, in parallel with measurement of skin temperatures overlying the supraclavicular (SCV) fossa and the lateral upper chest (control), before and after cold stimulation. Of the 29 scans, 20 were BAT positive after cold stimulation. The mean left SCV temperature tended to be higher in the BAT-positive group before and during cooling. It was significantly higher (P = 0.04) than the temperature of the control area, which fell significantly during cooling in the BAT-positive (-1.2 ± 0.3°C, P = 0.002) but not in the negative (-0.2 ± 0.4°C) group. The temperature difference (Δtemp) between left SCV and chest increased during cooling in the BAT-positive (1.2 ± 0.2 to 2.0 ± 0.3°C, P < 0.002) but not in the negative group (0.6 ± 0.1 to 0.7 ± 0.1°C). A Δtemp of 0.9°C conferred a positive predictive value of 85% for SCV BAT, superior to that of SCV temperature. The findings were similar on the right. In conclusion, the Δtemp is significantly and consistently greater in BAT-positive subjects. The Δtemp quantified by IRT after 2-h cooling shows promise as a noninvasive convenient technique for studying SCV BAT function.

Introduction

The recent years have seen a dramatic resurgence of interest in brown adipose tissue (BAT) in humans. Following the discovery of FDG‐avid depots in the supraclavicular and neck regions of humans undergoing Positron Emission Tomography (PET)‐CT scanning, investigations moved rapidly to histological confirmation of these depots as BAT (Zingaretti et al. 2009; Lee et al. 2011b) and then to define its contribution to energy expenditure. Indeed, efforts have now turned to exploring BAT as a novel therapeutic target in human metabolic disease.

Currently, PET‐CT is the gold standard method for the detection of BAT. However, PET‐CT has a number of limitations, including cost, radiation exposure, and duration of the procedure (Nedergaard and Cannon 2010; Lee et al. 2013). Diagnostic PET‐CT scans expose substantial radiation to the subject (Huang et al. 2009). While radiation exposure can be minimized in scans performed for research purposes, it will continue to limit the use of PET‐CT particularly for healthy volunteers. Furthermore, the reproducibility of PET‐CT imaging is poor (Lee et al. 2010) because of the susceptibility to changes and differences in scanning temperature and seasonal variations in temperature (Cohade et al. 2003; Au‐Yong et al. 2009; Zukotynski et al. 2009; Ouellet et al. 2011). Therefore, there is a need to develop other methods to investigate BAT physiology.

Because of the thermogenic properties of BAT, thermal imaging is a potential method for detecting BAT and studying its function. Using infrared thermography (IRT), we have previously reported a significantly higher skin temperature in the region overlying the supraclavicular (SCV) fossa than the mediastinal (MED) region in 87 individuals (Lee et al. 2011a). In one subject, this temperature difference became more pronounced following a meal ingestion and cold exposure, both known stimulants of BAT activity. Temperature increases within the supraclavicular region in response to cold exposure have also been confirmed with thermography in a recent study (Symonds et al. 2012). However, studies directly comparing and validating the results of IRT with PET‐CT imaging have not been published. The aim of this study was to assess the accuracy of IRT to predict the presence of BAT as benchmarked against PET‐CT imaging specifically to establish reproducibility, sensitivity, and specificity.

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Subjects and Methods

Subjects

Seventeen healthy subjects (12 men and five women) were studied after obtaining written informed consents. The mean age (±SD) was 36 ± 8 years and BMI was 25.4 ± 5.9 kg/m². Subjects were recruited to undergo PET‐CT scanning in a study to optimize conditions for the detection of BAT. In all subjects, PET‐CT scans were undertaken after a 6‐h fast and none had fasting hyperglycemia. The study was approved by the Human Research and Ethics Committee of Metro South Health Service.

Methods

All subjects underwent a 2‐h period of cooling at 19°C in an air‐conditioned room prior to intravenous administration of ¹⁸F‐FDG. Of the 17 subjects, 12 subjects were studied twice 2–3 weeks apart. This was done to determine the reproducibility of BAT detection under the 2‐h precooling conditions, collectively providing a total of 29 scans for analysis. We previously reported that the reproducibility of BAT detection under ambient conditions for routine diagnostic PET scanning (for malignancy staging) is low, being less than 15% (Lee et al. 2010). We treated each measurement as separate cases because the brown fat activity may vary within the same individual between different occasions of scanning. It was therefore important to determine whether the IRT measurements correspond to BAT activity on PET scans on every occasion.

PET‐CT imaging

All subjects were seated and rested comfortably in a quiet room during the procedure. PET imaging was undertaken at 1 h after intravenous injection of 75 MBq (2 mCi) of ¹⁸F‐FDG on a Biograph mCT 128 (Siemens Healthcare, Erlangen, Germany) equipped with time‐of‐flight electronics, in three‐dimensional list mode for 30 min in one bed position covering the skull base to the aortic arch. Noncontrast low‐dose CT (80 mAs) was subsequently performed for attenuation correction and localization of FDG‐avid sites. PET and CT image datasets were reconstructed in axial, coronal, and sagittal planes with a slice thickness of 4 mm. Images were interpreted by a dual‐qualified radiologist/nuclear physician using syngo.via software (Siemens Healthcare). BAT volume (in cm³) was quantified for right SCV and left SCV areas by autocontouring of FDG uptake above a set threshold (SUV_max 1.5) that show fat attenuation on CT. PET‐CT images for BAT were classified as “BAT Positive” or “BAT Negative” accordingly.

Infrared thermography

Subjects were seated in the upright position in an arm chair with head positioned in a neural position and the subject looking straight ahead. The upper torso from the chest area to neck region was exposed. A thermal imaging camera (FLIR B425, 3.1Mpixel, FLIR Systems Australia Pty Ltd, Melbourne, Vic., Australia) was used to acquire images of the anterior neck and upper chest region. The camera was positioned at the level of the neck 1 m from the subject's face. IRT was performed on all subjects at baseline ambient room temperature and then at 60 and 120 min during cooling at 19°C prior to PET‐CT scanning.

Using FLIR Research IR Professional Analyzing Software (Version 1.2, Wilsonville, OR), skin temperatures overlying the SCV fossa bilaterally and an area in the upper chest just lateral to the sternum approximating the second intercostal space (control) were determined for each image. Analyses were performed for both right and left SCV regions (Fig. ​(Fig.11).

Figure 1.

Mean (±SE) temperatures over the left (L) and right (R) supraclavicular areas (upper panel) and the anterior chest (lower panel) over 2 h of cooling at 19°C in an air‐conditioned room in subjects with positive and negative PET ...

For thermal analysis, the camera was set to fusion mode which displays a fusion of digital and thermal images. The emissivity was set to 0.98 for skin as detailed by the FLIR operations manual.

Temperature measurement of the SCV region was performed as follows. A circle, 2 cm margins, centered immediately above the midclavicle was selected for temperature analysis. In instances where there is a temperature gradient peak in the SCV fossa, there is a color gradation of increasing temperature from the periphery to the middle of the area. A target region of measurement was set around the first temperature contour gradient from the periphery and the highest temperature within this target area was recorded for later analysis. In images where no temperature gradient is apparent, an area above the midclavicle corresponding to the SCV fossa was selected for temperature measurement. This procedure was repeated for the chest control area. There was no difference in skin temperatures of the chest control areas on the left and the right. Analyses were undertaken using only the chest control readings on the left. iButtons are available as a means of recording skin temperature directly. iButtons were not used in this study because preliminary evaluation revealed excellent concordance between the camera and iButtons readings.

Statistical analysis

Statistical analysis was performed using SPSS version 21 (SPSS Inc., Chicago, IL, USA). Data are reported as mean ± SEM. The unpaired t‐test was used to compare data between the BAT‐positive and BAT‐negative groups. Two‐way repeated measures ANOVA was employed to determine whether temperatures changed significantly during cooling and whether this was influenced by PET status. Pearson correlation analysis was employed to test the relationships between different variables. Differences in temperatures during cooling between PET‐positive and PET‐negative groups were determined by two‐way ANOVA. The sensitivity and specificity of IRT were determined by ROC analysis. A P value <0.05 was considered significant.

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Results

Of the 29 scans performed after precooling, 20 were positive for BAT and nine were negative (Table 1). Of the 12 subjects who were studied twice, 10 had concordant scans (seven repeat positive, three repeat negative scans) while two subjects had discordant scans. 15 participants had repeat thermography images taken twice within 1 min on at least one occasion. The mean difference in recorded temperature between two images for 15 subjects was 0.06 ± 0.01°C with a CV of 0.009%.

Table 1.

Clinical characteristics of subjects categorized to BAT status as defined by PET‐CT imaging. Data are expressed as Mean ± SEM

SCV temperature

At baseline before cooling, mean left SCV temperature in the PET positive tended to be higher than that in the PET‐negative group (32.8 ± 0.3 vs. 31.5 ± 0.5, P =0.055) (Fig. ​(Fig.1).1). A similar trend was present on the right (32.4 ± 0.3 vs. 31.4 ± 0.6, P =0.06).

After 2‐h cooling, the mean SCV temperature remained higher in PET‐positive than PET‐negative group on both sides (left 32.3 ± 0.3 vs. 31.3 ± 0.5, P =0.08 and right 31.6 ± 0.3 vs. 31.0 ± 0.5, P =0.1), but the differences did not reach statistical significance nor did the temperature change significantly over the 2‐h cooling period (two‐way ANOVA) (Fig. ​(Fig.11).

Comparing left and right SCV temperatures, the mean skin temperature after 2‐h cooling overlying the left SCV was significantly higher than that on the right for both PET‐positive (32.3 ± 0.3 vs. 31.6 ± 0.3°C, P <0.001) and PET‐negative groups (31.3 ± 0.5 vs. 31.0 ± 0.5°C, P <0.01).

The mean BAT volume in the left SCV fossa was not significantly different to that of the right on the PET scan. SCV temperatures did not correlate significantly with SCV BAT volume.

Control chest temperature

At baseline before cooling, there was no difference in the mean chest temperatures between PET‐positive and PET‐negative groups (31.2 ± 0.4 vs. 30.9 ± 0.4) (Fig. ​(Fig.11).

After cooling, the mean chest temperature fell significantly by 1.2 ± 0.3°C (p = 0.002) in the PET‐positive but not in the PET‐negative (0.2 ± 0.4°C) group (Fig. ​(Fig.1)1) and the difference was significant between the groups (P =0.04). As cooling induced a greater fall in chest than in SCV temperatures, this resulted in a rise in the magnitude of temperature difference (temp) between the two sites.

Temperature difference between SCV and control chest areas (temp)

At baseline before cooling, in the BAT‐positive group, the mean SCV temperatures on both sides were significantly higher than the control chest temperature (Fig. ​(Fig.1).1). Similarly, in the BAT‐negative group, the mean SCV temperatures on both sides were also significantly higher than mean chest temperature. temp was greater in the BAT positive than in the negative group on the left (1.2 ± 0.2 vs. 0.6 ± 0.1°C, P =0.04) and right sides (1.0 ± 0.2 vs. 0.5 ± 0.01°C, P =0.04) (Fig. ​(Fig.2).2). The temp was significantly greater on the left than on the right side (P =0.004) in the BAT‐positive group.

Figure 2.

Upper panel shows Individual paired plots in PET‐positive (left panels) and PET‐negative (right panels) subjects studied before and after 2 h of cooling. The temperature difference (temp) between supraclavicular (left and right) ...

After cooling, temp between SCV and chest areas remained significantly greater in the BAT positive than the negative group on the left (2.1 ± 0.3 vs. 0.7 ± 0.1°C, P =0.002) and the right side (1.4 ± 0.3 vs. 0.4 ± 0.1°C, P =0.02). Moreover, for the 20 positive scans, the SCV BAT volume was significantly correlated with temp which was stronger on the left side (r = 0.57, P =0.004) than on the right (r = 0.49, P =0.02).

temp on the left rose significantly (1.2 ± 0.2 to 2.1 ± 0.3 °C; P = 0.04) in the BAT‐positive group but not in the BAT‐negative (0.6 ± 0.1 to 0.7 ± 0.1°C, P = 0.6) group (Fig. ​(Fig.2)2) with cooling. The change in temp on the right was similar, increasing in the BAT‐positive (1.0 ± 0.2 to 1.4 ± 0.3°C, P = 0.09) but not in the BAT‐negative (0.5 ± 0.1 to 0.4 ± 0.1°C) group. The temp change was greater on the left than right side throughout cold stimulation in the BAT‐positive group (P = 0.01). Figure 2 shows paired plots of temp on the right and left sides for each of the assessments in PET‐positive and PET‐negative scans.

Representative PET/CT scans and corresponding thermograms before and after cooling from two PET‐positive and a PET‐negative subject are shown in Figure 3. For the two positive scans, SCV temperatures are higher than the adjacent lateral areas before and after cooling. Note the fall in chest temperature after cooling. In the PET‐negative subject, the temperature in the SCV fossa is similar to the adjacent lateral area. There is no change in chest temperature after cooling.

Figure 3.

Representative PET‐CT and thermography images before and after 120‐min cooling for (A) two BAT‐positive subjects and (B) one BAT‐negative subject. In A, the thermograms for both PET‐positive subjects indicate that ...

ROC analysis

Before cooling, ROC analysis revealed that an optimal discriminant temp of >0.9 conferred a sensitivity of 77% and specificity of 78% on the left for a positive PET scan (Table 2). After 2 h of cooling, sensitivity increased to 87% with specificity unchanged. On the right, before cooling a Δtemp of ≥ 0.9°C conferred a sensitivity and specificity figures of 59% and 89% and after cooling, 68% and 78%, respectively. The sensitivities and specificities of SCV temperatures on the left and right were inferior to those of the corresponding temps. The corresponding positive and negative predictive values displayed in Table 2 show that the temp of 0.9 on the left side at 2 h of cooling confers a positive predictive value of 85% and negative predictive value of 77% for a positive PET scan. The sensitivity, specificity, and predictive values for SCV temperatures were inferior to corresponding temps regardless of the side of analysis.

Table 2.

Sensitivity and predictive value of infrared thermography for a positive PET/CT scan

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Discussion

IRT is an established and accepted technique for studying BAT function in small animals (Mccafferty 2007; Marks et al. 2009; Gilbert et al. 2012; Warner et al. 2013). Studies employing IRT to assess BAT activity in humans are beginning to emerge (Lee et al. 2011a; Symonds et al. 2012). However, the predictive value for detecting BAT by IRT in humans has not been rigorously assessed. This study investigated the usefulness of IRT as a tool for detecting BAT in humans by validating thermal imaging to that of positive PET‐CT imaging. During controlled cold stimulation in an air‐conditioned room, the mean skin temperature over the SCV fossa tended to be higher in the BAT‐positive group. Mean SCV temperatures were consistently higher than that over the chest in both BAT‐positive and BAT‐negative groups. The temperature difference between SCV and chest areas was significantly greater in the BAT‐positive group, correlated significantly with BAT volume and was consistently greater on the left. This temperature difference increased during cooling, an effect resulting from a fall in chest temperature rather than a rise in SCV temperature. The temp after cooling correlated significantly with BAT volume. The temperature difference was a better predictor of PET status than SCV temperature.

The observation that BAT‐positive subjects exhibited a fall in chest temperature compared to BAT‐negative counterparts is intriguing. It is conceivable that skin temperature over the chest provides afferent sensory input for the central activation of the SNS required to stimulate BAT function. Thus, a lower chest cutaneous temperature triggers a greater activation of BAT to maintain SCV temperature at a level which might have been lower in the absence of BAT activity. It is possible that the amount of body fat influenced the magnitude of the fall in chest temperature during cooling. Diminished thermal insulation could trigger the SNS, simultaneously stimulating BAT activity and causing peripheral skin vasoconstriction. As we did not measure body composition, we are unable to determine whether the proportion of body fat was lower in PET‐positive subjects.. Several studies have reported that body mass index is a negative predictor of BAT activity (Lee et al. 2013). In this study, mean body mass index tended to be lower among the PET‐positive subjects. Regardless of the mechanism(s), we have observed, in BAT‐positive subjects, higher SCV temperature, a greater skin temperature difference between BAT‐positive and ‐negative sites that is significantly correlated with the mass of BAT. These findings provide strong evidence that BAT in adult human is thermogenic and detectable by IRT.

We undertook a systematic ROC analysis of the impact of cooling on the value of SCV temperature and the temp in predicting BAT as identified by PET/CT. It is well established that cold stimulation enhances the detection and activity of BAT as assessed by PET‐CT imaging in humans. The rate of BAT detectability increases from 5–10% at ambient temperature to over 90% after cold stimulation (Lee et al. 2011b), a change equating to an increase in BAT activity of 10‐ to 15‐fold (Virtanen et al. 2009). SCV temperature was far inferior to temperatures and cooling had little effect because SCV temperatures did not change. In contrast, cooling enhanced the predictive value of temp on both left and right sides. Like PET‐CT, the diagnostic power of IRT is enhanced by cooling.

Another interesting observation was the consistently higher SCV temperature on the left than on the right, regardless of BAT status, and a greater temperature difference on the left between SCV and chest areas in BAT‐positive subjects. We observed no differences in SCV BAT mass between the sides nor asymmetry in the FDG uptake in those with positive scans suggesting that the lateralization is unlikely to be BAT related. The cooling technique is also unlikely to have had much bearing; subjects sat in an air‐conditioned room where cooling was spread evenly. Moreover, this temperature difference is evident before cooling and maintained throughout. We speculate that higher SCV skin temperature on the left arises from the unique vascular anatomy from a capacious brachiocephalic vein on the left.

IRT has several advantages. The procedure is convenient as it can be undertaken within minutes, unlike PET‐CT which requires a postinjection and scanning time of at least 2 h. Importantly, there is no radiation exposure with IRT, whereas for PET‐CT using a standard FDG dose, the radiation from a single study for oncological indications far exceeds radiation safety limits for investigative studies in normal subjects. Another useful application for IRT is in the monitoring of changes in BAT activity over minutes or hours that cannot be done with PET‐CT, which is restricted to a single time domain of capture over 1 h. In that regard, IRT offers considerable advantages as a safer and more flexible tool for studying BAT function and activity.

IRT conferred a probability of greater than 80% in predicting BAT. While promising, we caution against using IRT independent of PET‐CT imaging until more evaluations are published. At this stage, we propose that it serves as a screening tool for selecting subjects for regulatory studies of BAT function in whom the presence of significant BAT activity is initially confirmed by PET‐CT imaging. A baseline of PET positivity brings specificity to the interpretation of changes in temperature difference between the neck and chest as changes in BAT activity.

One of the limitations of IRT is that it is confined to studying BAT that is located superficially, particularly the SCV fossa. It is unlikely that thermogenicity of BAT located in deeper areas can be detected by IRT. Therefore, the study of BAT activity in humans by IRT is restricted to superficial BAT depots. Our study population is small and consisted of a heterogeneous group of individuals of varying age and BMI undergoing simultaneous PET‐CT scanning after cold stimulation by air conditioning. Future studies comprising larger numbers are required to provide more information on the value of IRT in the evaluation of BAT in humans.

This is the first study investigating the utility of IRT for detecting BAT referenced to PET‐CT. Our evaluation provides strong evidence that IRT is a promising noninvasive technique for the detection of BAT in the SCV region. The discriminant measure is the difference in skin temperatures between the SCV and the lateral chest areas, and not the temperature overlying the SCV fossa after cold stimulation. The predictive value of the temperature difference is better on the left than on the right. IRT is a useful technology that may complement PET‐CT imaging in the study of BAT in humans.

Thermographic evaluation of early melanoma within the vascularized skin using combined non-Newtonian blood flow and bioheat models.

Bhowmik A¹, Repaka R², Mishra SC³.

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Abstract

A theoretical study on vascularized skin model to predict the thermal evaluation criteria of early melanoma using the dynamic thermal imaging technique is presented in this article. Thermographic evaluation of melanoma has been carried out during the thermal recovery of skin from undercooled condition. During thermal recovery, the skin has been exposed to natural convection, radiation, and evaporation. The thermal responses of melanoma have been evaluated by integrating the bioheat model for multi-layered skin with the momentum as well as energy conservation equations for blood flow. Differential changes in the surface thermal response of various melanoma stages except that of the early stage have been determined. It has been predicted that the thermal response due to subsurface blood flow overpowers the response of early melanoma. Hence, the study suggests that the quantification of early melanoma diagnosis using thermography has not reached a matured stage yet. Therefore, the study presents a systematic analysis of various intermediate melanoma stages to determine the thermal evaluation criteria of early melanoma. The comprehensive modeling effort made in this work supports the prediction of the disease outcome and relates the thermal response with the variation in patho-physiological, thermal and geometrical parameters.

Motion tracking in infrared imaging for quantitative medical diagnostic applications.

Cheng TY, Herman C.

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Abstract

In medical applications, infrared (IR) thermography is used to detect and examine the thermal signature of skin abnormalities by quantitatively analyzing skin temperature in steady state conditions or its evolution over time, captured in an image sequence. However, during the image acquisition period, the involuntary movements of the patient are unavoidable, and such movements will undermine the accuracy of temperature measurement for any particular location on the skin. In this study, a tracking approach using a template-based algorithm is proposed, to follow the involuntary motion of the subject in the IR image sequence. The motion tacking will allow to associate a temperature evolution to each spatial location on the body while the body moves relative to the image frame. The affine transformation model is adopted to estimate the motion parameters of the template image. The Lucas-Kanade algorithm is applied to search for the optimized parameters of the affine transformation. A weighting mask is incorporated into the algorithm to ensure its tracking robustness. To evaluate the feasibility of the tracking approach, two sets of IR image sequences with random in-plane motion were tested in our experiments. A steady-state (no heating or cooling) IR image sequence in which the skin temperature is in equilibrium with the environment was considered first. The thermal recovery IR image sequence, acquired when the skin is recovering from 60-s cooling, was the second case analyzed. By proper selection of the template image along with template update, satisfactory tracking results were obtained for both IR image sequences. The achieved tracking accuracies are promising in terms of satisfying the demands imposed by clinical applications of IR thermography.

1. Introduction

1.1. Background

IR thermography is a non-ionizing and non-invasive imaging modality that has been regaining interest in clinical medicine in recent years. Such resurgence of interest can be attributed to the dramatic advances of IR camera and computer technology, novel image processing algorithms, and the progress in IR sensors since the 1990s. The availability of high sensitivity, high resolution IR detectors at a reasonable cost, along with miniaturization of the cameras, enabled the development of low-cost, portable systems suitable for quantitative diagnostic applications in medicine. IR diagnostic techniques rely on the hypothesis that a distinct thermal signature associated with a medical condition can be detected and quantified using modern camera technology. Quantitative imaging requires high accuracy – high sensitivity and high spatial resolution – measurements, which can be static or dynamic measurements, depending on the application. The common goal in thermographic diagnostic applications is to measure skin temperature as a function of location and time with high accuracy. These measurements are complex, since the shape of the surface, the surface properties and the motion of the subject will influence the temperature data derived from signals captured by the sensor. This paper addresses the influence of the motion of the subject on IR temperature measurements and proposes methods to compensate for these in order to achieve high-accuracy temperature measurements.

Infrared imaging can be implemented either as a static or as a dynamic technique in medical diagnostic applications. In static imaging applications a steady state situation is analyzed: the subject is typically exposed to normal ambient conditions and the spatial distribution of thermal contrasts on the body is measured and analyzed. Dynamic IR imaging detects both spatial and temporal variations of the emitted IR radiation, and this signal is related to skin temperature during postprocessing. Prior to image acquisition, a thermal excitation, such as cooling or heating, is applied to skin surface in dynamic IR imaging. During and following the thermal excitation a sequence of consecutive image frames is acquired, and the motion of the subject during the image acquisition poses a significant challenge for accurate surface temperature measurements. The phase after the removal of the thermal excitation is the thermal recovery process, which is frequently analyzed in medical imaging applications. By analyzing the thermal recovery of the skin temperature, with temperature variations in the range of hundreds of millikelvins, abnormalities such as the malignancy of skin lesions can be examined, quantified and potentially diagnosed (with appropriate calibration and clinical validation) [1–7].
During the acquisition period, the patients’ involuntary movements (breathing or small involuntary movements of the body and limbs) will hinder the accurate temperature recording at any particular point on the skin, therefore undermining the validity and accuracy of the local temperature analysis and the associated diagnosis. As shown in Fig. 1, in clinical applications of medical IR imaging [2,4], the patient is typically motionless, positioned on a fixed exam chair or bed, and the IR camera is oriented towards the lesion on the skin with the camera axis perpendicular to the skin surface. Since the out-of-plane movements of the patient are limited and restrained by the exam chair, they result in a predominantly linear in-plane motion in the image sequence, as illustrated in Fig. 2. This linear in-plane motion is considered to be the major source of motion encountered in the clinical IR image analysis.

Fig. 1

Schematic of subject's motion due to respiration and small involuntary movements of the body and limbs in the clinical IR imaging environment with the patient positioned in the exam chair.

Fig. 2

Sample infrared images illustrating the subject's in-plane motion in the acquired IR image sequence. The white arrows show the motion direction relative the previous frame (the adjacent frame on the left hand side), the rectangle is the template of known ...

Although such in-plane motion is relatively small and limited in range, it still hinders the successive measurement of temperature at a particular location on the skin surface in the IR image sequence. For example, early stage melanoma lesions are small (of the order of a few millimeters), and therefore movements of the order of a millimeter will hinder the localization of any subtle thermal features [5]. The measured temperature differences are often very small as well (fraction of a degree) and the feature to be analyzed (the lesion) cannot be detected directly in the IR image, prior to image processing. In static IR imaging, in which the temperature of the skin is approximately constant over time, accurate tracking can locate the lesion automatically in the IR image without the aid of a visible landmark. In dynamic IR imaging the temperature of the skin and the signal detected by the camera sensor change over the time [2–4]. In such applications motion tracking is crucial to record the temperature evolution accurately over the image sequence.

1.2. Literature review

Tracking a moving object in an image sequence is an active topic in civil and military applications. Initial applications were developed for white light imaging, however, motion and target tracking in IR image sequences has become the subject of increasing interest in recent years as well. Due to the lower cost and fast improvement of infrared (IR) technology, object tracking has also been widely used in IR imaging, such as pedestrian detection for surveillance purposes [8–10]. In IR surveillance applications the target typically stands out against the background, it often covers a relatively small fraction of the total image frame area, the range of motion is large and accurate temperature information is generally not of interest.

Only a few researchers have tackled the motion tracking challenge in IR imaging in medical applications [5,6]. The key difference between surveillance and medical applications is the smaller thermal contrast between the diseased and healthy tissue, which is often not detectable without sophisticated image processing. Also, the target often covers a significant portion of the image frame. The range of motion of the subject is smaller and accurate quantitative detection of temporal as well as spatial variations is essential. Most of the past clinical applications of IR imaging focused on breast cancer detection. To enable the wide use of dynamic IR imaging in breast cancer screening [11–13], the motion artifact reduction approach using image sequence realignment was proposed [14–16] with marker-based image registration. Image registration involves transforming different data sets into one coordinate system. In medical imaging and computer vision applications registration is essential in order to allow the comparison or integration of data obtained from different measurements and sources, in our application from white light and infrared images. In breast cancer imaging [14–16], the registration of each IR image frame is achieved by aligning 5–18 markers (which are directly visible in the successive images) to serve as control points in the IR image sequence. The approach can achieve accurate tracking performance in terms of signal to noise ratio (SNR).
In the Heat Transfer Lab of Johns Hopkins University, we have developed a dynamic IR imaging system for the detection of melanoma [2–7]. The system relies on thermo-stimulation with external cooling and compares the transient thermal response of cancerous lesions and healthy skin. As shown by Cetingul et al. [5] and Herman [6], based on the quadratic motion model for landmark-based registration, a sequence of IR images acquired during the thermal recovery phase can be aligned to compensate for involuntary motion of the patient. The motion compensation enables an accurate measurement of temperature differences between lesions and healthy skin, and provides quantitative information to identify the malignancy of lesions. Without motion tracking, the measurement errors are too large to detect temperature differences that are indicative of malignancy [4–6]. Based on the experiences gained in our previous clinical studies, in this study, a template-based tracking scheme is applied for the dynamic IR imaging.
In the field of computer vision, automatic methods for tracking the moving object in an image sequence fall into three main categories [17,18]: feature-based tracking [19,20], contour-based tracking [21,22], and region-based tracking [23,24]. In the absence of occlusions, region-based tracking methods perform well in terms of robustness and accuracy. The template-based tracking [25] falls into the category of region-based tracking methods. When compared with other region-based methods with non-parametric description of the region's content [26], the template-based tracking directly uses a region content of the image to track the moving object. This is accomplished by extracting a template region in the first frame and finding the most matching region in the following frames. In this way the moving object can be tracked in a video sequence.

The template-based tracking method is originally based on the assumption that the object's appearance remains constant throughout the video sequence. However, in practical IR imaging applications this assumption often holds only for a certain period of time. The appearance of the object would change significantly with time and changes in the environment, for example as the temperature of the object changes during thermal recovery in dynamic IR imaging. Considering the tracking error due to the violation of initial assumption, improvements in the tracking algorithm, including template update [27] and the inclusion of robust weighting mask for template matching [18], are proposed in this paper. Based on our preliminary results [28], in the study we introduce the tracking method and validate it on two characteristic classes of problems relevant for medical applications, the static and dynamic imaging of skin temperature.

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2. Algorithms and methods

2.1. Template-based tracking algorithm

In the clinical application of IR imaging a marker (a rectangular shape applied to skin in our experiment, Fig. 3b) is commonly used for the registration of the skin lesion [2,4]. This marker can simultaneously serve as the landmark of consistent appearance in the template region. The template image is a sub-region of an image which contains the object (skin lesion, for example) to be tracked in the image sequence. Template-based tracking is achieved by estimating coordinate alignment between the template image and the consecutive frames in a given video sequence. Since the involuntary movements of patient are mainly in-plane motions with a limited range, the template-based method is well suited for motion tracking in medical IR imaging. In this paper, the method is described using the notation of Matthews et al. [27].

Fig. 3

(a) The Merlin midwave infrared camera and the IR image acquisition system; (b) Two paper adhesive markers: the square one serves as the tracking template and the round one represents the simulated lesion for error analysis; (c) IR image of the adhesive ...

The alignment of images in a sequence can be parameterized as a warp function, which transforms the pixel coordinate x in the template image to a new coordinate W(x;p) in the subsequent image frame. In this expression p denotes the transformation parameters p = (p₁,. . .,p_n)^T of the warp function. In our application, a sub-region containing the object in the initial IR image frame I₀(x) is extracted to be the template image T(x). In a subsequent image frame I(x), the template image content at pixel x:T(x), will be warped by W(x;p) and presented as I(W(x;p)). It would have similar content as its correspondence in the template image if the tracking is valid. Therefore, the optimized parameters p will be searched to find the best match as

I(W(x;p))≈T(x).

(1)

In template-based tracking, the 2D affine transformation is utilized as the warp function W(x;p). Linear transformations, including translation, rotation, shear mapping, and scaling, are all taken into account, and any two parallel lines will remain parallel after the transformation. The affine warp consists of 6 independent parameters (Section 2.2):p = (p₁, p₂, p₃, p₄, p₅, p₆)^T to model the transformation as

W(x;p)=(1+p1p2p31+p4p5p6)⎛⎝xy1⎞⎠,

where (p₅, p₆) are two parameters describing translation, (p₁, p₄) are two parameters for the scaling of x and y, and (p₂, p₃) describe the angular change of each axis after warping.

2.2. Lucas–Kanade approach

The first use of image alignment with a template reported in the technical literature is the Lucas–Kanade optical flow algorithm [25]. Since this work, the approach has become one of the most widely used methods in object tracking. In the remainder of this section, the notation of Baker and Matthews [29] is used to describe the algorithm. In order to search for the optimized parameters p for the warp function, the objective of Lucas–Kanade algorithm is to minimize the sum of square errors between the image content of I(W(x;p)) and the template T(x) as

∑x[I(W(x;p))−T(x)]2.

(3)

The minimization of Eq. (3) is a non-linear optimization problem. Based on a current estimate p for the warping parameters, the Lucas–Kanade algorithm iteratively solves for an increment Δp to update the current estimation using

Pnew←p+Δp,

(4)

and the expression (3) can be re-written as the error function

∑x[I(W(x;p+Δp))−T(x)]2.

(5)

The iteration process will continue until the estimate of p converges, and the converged estimate will serve as the new set of optimized parameters p for the warp function W(x;p).
Furthermore, to linearize the error function (5), the first-order Taylor expansion is used to approximate I(W(x;p + Δp)) as

I(W(x;p+Δp))=I(W(x;p))+∇I∂W∂pΔp

(6)

Therefore, by substituting I(W(x;p + Δp)), Eq. (6), into (5), the error function can be written as [29]

∑x[I(W(x;p))+∇I∂W∂pΔp−T(x)]2,

(7)

where ∇I is the gradient of image I evaluated when the current warp function W(x;p) is applied, and ∂W∂p represents the Jacobian of the warp.
The affine transformation W(x;p) can be decomposed as W(x;p) = (W_x, W_y)^T, and the Jacobian of W(x;p) is represented as

∂W∂p=⎛⎝⎜⎜⎜∂Wx∂p1∂Wy∂p1⋯⋯∂Wx∂p6∂Wy∂p6⎞⎠⎟⎟⎟=(x00xy00y1001).

Based on Eq. (8), to minimize the error function (7), we first determine its partial derivative and set it to zero. Next, the closed-form solution for the Δp can then be obtained as [29]

Δp=H−1∑x[∇I∂W∂p]T[T(x)−I(W(x;p))].

(9)

In Eq. (9) H is the n × n Hessian Matrix and the Gauss–Newton approximation is adopted for Hessian Matrix as

H=∑x[∇I∂W∂p]T[∇I∂W∂p].

(10)

2.3. The weighting function for robust tracking

Constant brightness over the entire image sequence is the primary assumption for template-based tracking. This assumption is often violated when the brightness variations are unavoidable, such as the imaging of transient processes. In our application the assumption can hold for the static IR image sequence. For the dynamic IR image sequences this assumption will be violated, since the purpose is to analyze the time-varying thermal signal of a skin lesion recovering from a cooling excitation.

Therefore, to tackle the problem of brightness variation in dynamic IR image data, the robust version of Lucas–Kanade tracking algorithm [18,30,31] is implemented in our application. The robust version adds a weighting mask to the template pixels in the computation of the least square process. In this algorithm the pixels with brightness change in the template image will be treated as outliers, and their contribution will be suppressed in the computation. Furthermore, to improve the efficiency of the Lucas–Kanade algorithm, the inverse compositional algorithm [29–31] is also implemented in the robust version. In the inverse compositional algorithm the roles of template image T(x) and subsequent image I(x) are interchanged. Based on the notation of [30,31], a weighing mask M(x) of the same dimension as the template image is included in the least square process to replace Eq. (5)

∑xM(x)⋅[I(W(x;p+Δp))−T(X)]2.

(11)

The closed-form expressions for the increment Δp, described by Eqs. (9) and (10), will be replaced by Eqs. (12) and (13) in the robust version of Lucas–Kanade tracking algorithm as:

Δp=H‒−1∑xM(x)⋅[∇T∂W∂p]T[I(W(x;p))−T(x)]and

(12)

H‒=∑xM(x)⋅[∇T∂W∂p]T[∇T∂W∂p].

(13)

2.4. Infrared image acquisition

To test the tracking performance of the algorithm, a subject's left hand with deliberate random in-plane motion is imaged using an IR camera. Two types of IR image sequences were tested in this study:

1. Steady-state IR image sequence: In the first imaging experiment, the temperature of the skin does not change with time, therefore the basic tracking performance can be tested under the constant brightness assumption.
2. Thermal recovery IR image sequence: In our previous clinical application [2], the thermal recovery IR image sequence after cooling reveals critical information to detect the malignancy of melanoma lesions. Thus in the second experiment, we first applied cooling to the skin for 60s, and acquired the IR video after the cooling was removed. The temperature changes cause significant changes of brightness in the IR image sequence. Using these test images, the robustness of the tracking algorithm can be tested for the situation when the skin temperature changes with time.

As shown in Fig. 3(a), the camera used in the experiment is a Merlin midwave (3–5 μm) infrared camera (MWIR) (FLIR Systems Inc., Wilsonville, OR), which has the temperature sensitivity of 0.025 °C. A 320 × 256 pixel focal plane array (FPA) is used to acquire 16 bit raw data at frame rate of 60 Hz. Each image frame has the field of view (FOV) of 22 × 16 degrees. Using the parameters obtained from black body calibration [32], the camera can generate an IR thermal image calibrated as temperature value.

2.5. Template image and the simulated lesion

In general, a skin lesion (early stage melanoma is of particular interest in the present study) is not directly identifiable in the IR image because the temperature difference between the lesion and the healthy skin is typically very small under steady state conditions, i.e. it is of the order of natural temperature fluctuations of the skin. The temperature difference increases during dynamic imaging, which imposes the need to track a particular physical location on the skin throughout an image sequence. To implement the template-based algorithm and evaluate its performance, we created two paper markers which are both visible in the IR image (Fig. 3(b) and (c)). One of them is a square marker of size 5 cm × 5 cm, which serves as the constant feature in the template image as shown in Fig. 3(c). The second one is a small, round maker, which is easy to segment in IR image, such that we can simulate the lesion location and evaluate the tracking results quantitatively.

2.6. Weighting mask for the template image

As introduced in Section 2.3, to implement the robust version of Lucas–Kanade algorithm [18,29–31], we used the normalized intensity of the template image as the weighting mask. In the template image, the image brightness is normalized in the range (0,1) with respect to the lowest and the highest temperature value. This normalized image serves as the weighting mask M(x) in Eq. (11). The weighting mask treats a pixel x as reliable if M(x) = 1, and a pixel x is considered an outlier if M(x) = 0. The two weighting masks for the steady-state and thermal recovery IR image sequences are displayed in Fig. 4a and b. It can be observed that the skin region outside the square marker, which has higher and more constant temperature throughout the IR video, will be highly weighted by the weighting mask (Fig. 4b, light colored region). On the other hand, the region exposed to cooling in the thermal recovery template image (Fig. 4b, darker region), which has lowest temperature but highest brightness variation rate, will be suppressed as unreliable outlier pixels by the weighting mask. This example explains the reason why the normalized temperature image is taken as our weighting mask. The robust Lucas–Kanade algorithm with pixel weighting was implemented using the public domain MATLAB function shared by Dir-Jan Kroon [33].

Fig. 4

(a) Weighting mask of the template image for the steady-state IR image sequence, (b) weighting mask of the template image for the thermal recovery IR image sequence (created after the cooling is applied), (c) the appearance of the template images after ...

2.7. Determining the location of the simulated lesion for tracking error analysis

In order to evaluate and quantify the tracking performance of our algorithm, we need to know the actual location of the simulated lesion (round marker) in order to be able to compare it with the estimated location determined by the motion tracking algorithm. For this reason the simulated lesion, which is clearly visible in the IR image sequence, is used in the analysis as opposed to a real (cancerous or benign) skin lesion, which usually cannot be detected directly in the IR image. The centroid point of the simulated lesion is taken as the reference coordinate, and it is the mean value of the pixel coordinates of the marker's border. The coordinates of the border of the simulated lesion were determined using an outline segmentation algorithm – a random walker [34]. By selecting two points inside/outside of the closed region of an object, the random-walker algorithm can automatically segment the object's outline. After the simulated lesion outline is delineated, its centroid location can be obtained as the reference point.

When the tracking algorithm is applied, the centroid location (segmented in the initial frame) will be predicted in the subsequent frames based on the parameters obtained by solving Eq. (12). Finally, when knowing both the actual (from image segmentation) and predicted (from the tracking algorithm) centroid locations in the image sequence, the Euclidean distance between them is computed to quantify the tracking error.
In Experiment A (steady state), since no cooling is applied to the skin, the entire region of simulated lesion is visually identifiable throughout the IR image sequence. Therefore, the outline of the simulated lesion can be directly segmented in each frame (Fig. 5) using random-walker algorithm.

Fig. 5

(a) Adhesive markers and the segmented outline of the simulated lesion (red circle); (b) Red cross: the centroid location of the simulated lesion. (For interpretation of the references to colour in this figure legend, the reader is referred to the web ...

In Experiment B (during thermal recovery) the border of the simulated lesion is obstructed (masked) by the cooling spot, therefore the region of the simulated lesion is no longer clearly visible in the IR image (Fig. 6b) and the lesion outline cannot be segmented directly. Therefore we had to apply registration between the IR images before and after cooling in order to determine the lesion outline in the thermal recovery image sequence. To accomplish this, we used the steady-state image of the simulated lesion before cooling, for which the random-walker algorithm can be directly applied, to segment the outline. This lesion outline is then registered to the recovery image sequence via the location of four corners of the square marker, which are visible in both images before and after cooling.

Fig. 6

Registration of the simulated lesion from the steady-state IR image (a) to the thermal recovery IR image (b) in Experiment B. The color change from (a) to (b) within the rectangular template is characteristic for the cooling process in dynamic IR imaging. ...

As shown in Fig. 6a, the lesion is first segmented in the image before cooling. Next, by identifying the location of four corners of the window marker in the images before and after cooling (Fig. 6b), a 2D projective transformation matrix is solved [35] based on the four corners correspondences. The identification of the four corners can be accomplished either manually or automatically using corner-detecting algorithms such as the Harris detector [36] or the Shi and Tomasi [37] method. The registration relation between these two frames can thus be built, and then the lesion outline segmented in the image before cooling can be mapped into the image after cooling (Fig. 6b). By applying the registration process consecutively to individual image frames in a sequence, the actual location of the lesion centroid can be determined for each frame of the thermal recovery sequence.

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3. Results

3.1. Experiment A: Tracking performance for a steady-state IR image sequence

We applied the algorithm described in Section 2 to 23 frames of typical steady-state IR images with random in-plane motion incorporated. The tracking results, represented by 3 pairs of adjacent frames, are shown in Fig. 7. The magnified views of the predicted (circle) and actual (cross) locations of the simulated lesion are also shown in Fig. 7. As we compare the location predicted by the algorithm with the actual location measured by the segmentation, it can be seen that despite the quite significant random in-plane motion, the two locations are very close to each other and the deviations are minimal.

Fig. 7

Tracking results for Experiment A in a steady-state image sequence (circle – location of the centroid predicted by the tracking algorithm, cross – actual centroid location of the simulated lesion). The magnified view of the simulated lesion ...

To evaluate the tracking error quantitatively, the Euclidean distance between the predicted and actual location of the lesion centroid in units of pixels is calculated for each frame, and the results are plotted as blue bars in Fig. 8. In addition, the displacement of the lesion centroid with respect to the previous frame (the displacement from frame i-1 to i) is also recorded in units of pixels. As evident from Fig. 8, despite of the fact that the frame-to-frame displacement can exceed 35 pixels, the tracking errors over the entire image sequence are smaller than 3 pixels. This result suggests that the tracking algorithm performs very well even for relatively large in-plane motion. To translate these data into real-life dimensions, the actual size of marker is introduced: 30 mm correspond to 86 pixels in the IR image. Therefore we can infer that 1 pixel approximately corresponds to 0.35 mm on the skin surface. Using this conversion, the highest tracking errors of 3 pixels correspond to 1.05 mm in the real dimensions. The displacement of 35 pixels corresponds to approximately 11 mm motion amplitude in terms of physical dimensions, which is significant. In medical applications the motion amplitude of the subject is typically much smaller, of the order of a few millimeters. The measurement accuracy and spational resolution can be further improved by improving the spatial resolution of the focal plane array of the camera.

Fig. 8

Tracking error analysis for the steady-state image sequence in experiment A: the red line represents the frame-to-frame displacement (from frame i-1 to frame i) of the lesion centroid, and the blue bars represent the Euclidean distance between the predicted ...

3.2. Experiment B-1: Tracking performance for the thermal recovery IR image sequence (initial time interval, within 30 s into thermal recovery)

The tracking performance for the image sequence recovered from dynamic IR imaging with cooling is illustrated by the 3 pairs of adjacent frames in Fig. 9. In this trial, the first 23 IR images after the removal of the cooling excitation are considered. The frame rate is 1 frame/s, and the quantitative analysis of the tracking error at each frame is shown in Fig. 10. It can be observed that despite of the moderate brightness variations during thermal recovery, the lesion centroid can still be tracked with reasonable accuracy within the first 23 frames. This corresponds to 23 s into the thermal recovery phase, and the highest tracking errors are smaller than 4 pixels. The results suggest that, despite of the moderate brightness variations, the template-based algorithm performs with good accuracy in the first 23 s into the thermal recovery sequence. This is accomplished by taking the target region in the first frame as the template image, along with its normalized image, as the weighting mask.

Fig. 9

Tracking performance of Experiment B-1 for the first 23 frames in the thermal recovery IR image sequence (circle – centroid location predicted by the tracking algorithm, cross – actual centroid location of the simulated lesion).

Fig. 10

Tracking error analysis (Experiment B-1) of the thermal recovery image sequence after the removal of cooling: the red line represents the frame-to-frame displacement (from frame i-1 to frame i) of the simulated centroid. The blue bars represent the Euclidean ...

3.3. Experiment B-2: Tracking performance for the thermal recovery IR image sequence (over 30 s into the thermal recovery phase) without template update

As we extended the tracking test over 60 frames (1 min) in the second trial, the tracking performance of the algorithm described in Section 3.2 deteriorated. The four frames shown in Fig. 11a illustrate the dramatic changes the IR image undergoes during thermal recovery. As a consequence of these changes, the tracking algorithm gradually loses the track of simulated lesion between frames 41 and 65. The error analysis in Fig. 11b indicates a dramatic increase of tracking errors after frame 29. The significant error growth can be attributed to the brightness inconsistency after 30 s into the thermal recovery phase, when the thermal appearance of the cooled region becomes substantially different from the template image recorded at the first frame. As indicated in Fig. 11b, the tracking errors exceed 5 pixels after frame 41. Therefore, to ensure the robustness the tracking method, adjustments to the template-based algorithm were needed. These adjustments are expected to deal with the brightness inconsistencies encountered during the thermal recovery.

Fig. 11

Tracking performance for Experiment B-2 with tracking duration of 90 s into the thermal recovery phase without template update: (a) four representative image frames illustrating the differences between centroid location predicted by the algorithm and ...

3.4. Experiment B-3: Tracking performance of the thermal recovery IR image sequence with template update for long imaging times

To reduce the growth of tracking errors due to brightness inconsistency after 30 s into the thermal recovery, instead of a using single template image taken at the initial time, we update the template image and the weighting mask at frame 29 as illustrated in Fig. 12. Frame 29 is chosen because it is the last frame with errors smaller than 5 pixels, before significant error growth is observed in Fig. 11b. This image frame has similar brightness as the rest of the frames recorded after 30 s. Thus the tracking procedure for longer imaging times has two stages: in the first stage the images from frames 1–29 are tracked using the initial template image. In the second stage, after the update at frame 29, the remaining frames are tracked using the updated template image (Fig. 12).

Fig. 12

(a) Updated template image, frame 29, (b) updated weighting mask for frame 29 and (c) resulting temperature value for the updated template image after applying the weighting mask.

The improvements achieved with the template update incorporated into the tracking algorithm are presented in Fig. 13. In Fig. 13a, we can observe that the simulated lesion can be tracked correctly in the image after frame 30. The improved accuracy is obvious when compared with its counterpart shown in Fig. 11a (without template update). The tracking error (blue bars) and displacement magnitude (red line) charts for the experiment using template update are shown in Fig. 13b. When compared with the data in Fig. 11b, it is evident that the tracking errors are significantly reduced, under 5 pixels, throughout the image sequence. The results suggest that, when applying the tracking algorithm to the thermal recovery image sequence, updating the template information at one (or more) selected time instant(s) can improve the tracking robustness for longer imaging times despite the brightness inconsistencies caused by cooling.

Fig. 13

Tracking performance of Experiment B-3 for 90 image frames with a template update carried out at frame 29: (a) Four representative frames showing the location differences between the predicted and actual lesion centroid and (b) the tracking errors at ...

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4. Conclusions

In this study, we demonstrated the feasibility of template-based algorithm for involuntary motion tracking in in-vivo infrared imaging. In the presence of random in-plane motion, we demonstrated that the robust version of Lucas–Kanade algorithm can track the location of the target region in IR images sequences with good accuracy for sequences with small temperature changes (steady state). When applying the algorithm to the steady-state IR image sequence, satisfactory results were obtained when a single template information is used, detected at the first frame.

As discussed for Experiment B-1, the tracking scheme using a single template image can also achieve accurate tracking results during the first 30 s into the thermal recovery image sequence. The results suggest that the weighting mask, created by normalizing the template image at the initial frame, can effectively ensure the tracking robustness in the early stage of the thermal recovery phase (0–40 s). During this period the temporal change of skin temperature is highest and the thermal signature of lesion most pronounced [38].
As the duration of the thermal recovery exceeds 30 s, at later times the brightness of the cooled region differs significantly from the initial template image, which causes the tracking errors to grow significantly, as demonstrated in Experiment B-2.

From the improved tracking results obtained in Experiment B-3, we can infer that by updating the template information at the end of the early stage (around 30 s), tracking robustness can be ensured throughout a longer image sequence. The approach reveals that in dynamic IR imaging applications the template update can be an effective amendment to stabilize and improve the tracking performance of the template-based algorithm.