Performance Assessment of Low-Cost Thermal Cameras for Medical Applications

Abstract

Thermal imaging is a promising technology in the medical field. Recent developments in low-cost infrared (IR) sensors, compatible with smartphones, provide competitive advantages for home-monitoring applications. However, these sensors present reduced capabilities compared to more expensive high-end devices. In this work, the characterization of thermal cameras is described and carried out. This characterization includes non-uniformity (NU) effects and correction as well as the thermal cameras’ dependence on room temperature, noise-equivalent temperature difference (NETD), and response curve stability with temperature. Results show that low-cost thermal cameras offer good performance, especially when used in temperature-controlled environments, providing evidence of the suitability of such sensors for medical applications, particularly in the assessment of diabetic foot ulcers on which we focused this study.

Noncontact Body Temperature Measurement: Uncertainty Evaluation and Screening Decision Rule to Prevent the Spread of COVID-19

“…As an example, when the emissivity is equal to 0.98 and the mean radiant temperature is 10 °C higher or lower than the body temperature (i.e., outdoor conditions), an error of about 0.2 °C will occur. On the other hand, these errors cannot be corrected through a simple calibration, since this depends on the reflected ambient temperature (e.g., from a window that is irradiated by the sun), even if the room temperature is stable [ 9 , 10 ]. The use of a reference target (i.e., at known temperature) may reduce and compensate for the effect of this influence factor.…”

“…In fact, such measurement is particularly influenced by the unavoidable instrumental uncertainties and by the operator’s ability, but also by numerous “influence quantities”, such as (i) the emissivity and the reflection coefficient of the emitting skin surface [ 6 ]; (ii) the transmission coefficient of the medium between the sensor and the target; (iii) the average radiant temperature of the measurement environment (i.e., the reflected temperature); (iv) the distance and consequent size of the target (effect of the size of the source) [ 7 , 8 ]. However, the accuracy of noncontact temperature measurement can be improved by utilizing dual-band or multiband infrared sensing [ 9 , 10 ]. In fact, these sensors, although more costly and complicated, provide compensation of unknown emissivity and of some background noise, since the infrared emitted from the target at different wavelength bands is detected.…”

Review of the efficacy of infrared thermography for screening infectious diseases with applications to COVID-19

“…Older IRT systems reported NETD in the 70- to 150-mK range, whereas current systems, including compact low-cost technologies, report NETD of . 30 , 34 , 35 …”

Segmentation Approaches for Diabetic Foot Disorders

“…RGB-D images, consisting of Red, Green and Blue color space plus depth information, were acquired with an Intel ® RealSense ™ D415 camera (Intel Corporation, Santa Clara, CA, USA). IR images were acquired with a low-cost thermal camera model TE-Q1 Plus from Thermal Expert ™ (i3system Inc., Daejeon, Republic of Korea) which was previously described and calibrated [ 18 ]. These cameras were assembled together in a customized support, manufactured in a 3D printer, that kept the cameras horizontally aligned.…”

“…First, high-end infrared cameras are considerably expensive as the ones used in astrophysics [ 8 , 9 ]. Low-cost devices, based on microbolometers, can provide similar features for the required medical application under controlled ambient environment [ 18 ]. Second, a fully unsupervised, without end-user interaction, and automatic segmentation of the feet sole is critical since manual segmentation is dependant on the observer as well as an extremely time-consuming task.…”