Infrared thermal imaging for automated detection of diabetic foot complications

Background Although thermal imaging can be a valuable technology in the prevention and management of diabetic foot disease, it is not yet widely used in clinical practice. Technological advancement in infrared imaging increases its application range. The aim was to explore the first steps in the applicability of high-resolution infrared thermal imaging for noninvasive automated detection of signs of diabetic foot disease. Methods The plantar foot surfaces of 15 diabetes patients were imaged with an infrared camera (resolution, 1.2 mm/pixel): 5 patients had no visible signs of foot complications, 5 patients had local complications (e.g., abundant callus or neuropathic ulcer), and 5 patients had difuse complications (e.g., Charcot foot, infected ulcer, or critical ischemia). Foot temperature was calculated as mean temperature across pixels for the whole foot and for specified regions of interest (ROIs). Results No diferences in mean temperature >1.5 °C between the ipsilateral and the contralateral foot were found in patients without complications. In patients with local complications, mean temperatures of the ipsilateral and the contralateral foot were similar, but temperature at the ROI was >2 °C higher compared with the corresponding region in the contralateral foot and to the mean of the whole ipsilateral foot. In patients with difuse complications, mean temperature diferences of >3 °C between ipsilateral and contralateral foot were found. Conclusions With an algorithm based on parameters that can be captured and analyzed with a high-resolution infrared camera and a computer, it is possible to detect signs of diabetic foot disease and to discriminate between no, local, or difuse diabetic foot complications. As such, an intelligent telemedicine monitoring system for noninvasive automated detection of signs of diabetic foot disease is one step closer. Future studies are essential to confirm and extend these promising early findings.

Improved infrared thermal imaging of a CMOS MEMS device

We report a technique which can be used to improve the accuracy of infrared (IR) surface temperature measurements made on MEMS (Micro-Electro-Mechanical- Systems) devices. The technique was used to thermally characterize a SOI (Silicon-On-Insulator) CMOS (Complementary Metal Oxide Semiconductor) MEMS thermal flow sensor. Conventional IR temperature measurements made on the sensor were shown to give significant surface temperature errors, due to the optical transparency of the SiO 2 membrane layers and low emissivity/high reflectivity of the metal. By making IR measurements on radiative carbon micro-particles placed in isothermal contact with the device, the accuracy of the surface temperature measurement was significantly improved. © 2010 EDA Publishing/THERMINIC.

Use of thermal imaging in sports medicine research : a short report

The assessment of skin temperature (Tsk) in athletic therapy and sports medicine research is an extremely important physiological outcome measure.Various methodsof recording Tsk, including thermistors, thermocouples and thermocrons are currently being used for research purposes. These techniques are constrained by their wires limiting the freedom of the subject, slow response times, and/or sensors falling off. Furthermore, as these products typically are directly attached to the skin and cover the measurement site, their validity may be questionable.This manuscript addresses the use and potential benefits of using thermal imaging (TI) in sport medicine research.Non-contact infrared TI offers a quick, non-invasive, portable and athlete-friendly method of assessing Tsk. TI is a useful Tsk diagnostic tool that has potential to be an integral part of sport medicine research in the future. Furthermore, as the technique is non-contact it has several advantages over existing methods of recording skin temperature

The use of thermal imaging in assessing skin temperature following cryotherapy : a review

Nineteen studies met the inclusion criteria. A skin temperature reduction of 5–15 °C, in accordance with the recent PRICE (Protection, Rest, Ice, Compression and Elevation) guidelines, were achieved using cold air, ice massage, crushed ice, cryotherapy cuffs, ice pack, and cold water immersion. There is evidence supporting the use and effectiveness of thermal imaging in order to access skin temperature following the application of cryotherapy. Thermal imaging is a safe and non-invasive method of collecting skin temperature. Although further research is required, in terms of structuring specific guidelines and protocols, thermal imaging appears to be an accurate and reliable method of collecting skin temperature data following cryotherapy. Currently there is ambiguity regarding the optimal skin temperature reductions in a medical or sporting setting. However, this review highlights the ability of several different modalities of cryotherapy to reduce skin temperature.

Towards robust night and day place recognition using visible and thermal imaging

The chief challenge facing persistent robotic navigation using vision sensors is the recognition of previously visited locations under different lighting and illumination conditions. The majority of successful approaches to outdoor robot navigation use active sensors such as LIDAR, but the associated weight and power draw of these systems makes them unsuitable for widespread deployment on mobile robots. In this paper we investigate methods to combine representations for visible and long-wave infrared (LWIR) thermal images with time information to combat the time-of-day-based limitations of each sensing modality. We calculate appearance-based match likelihoods using the state-of-the-art FAB-MAP [1] algorithm to analyse loop closure detection reliability across different times of day. We present preliminary results on a dataset of 10 successive traverses of a combined urban-parkland environment, recorded in 2-hour intervals from before dawn to after dusk. Improved location recognition throughout an entire day is demonstrated using the combined system compared with methods which use visible or thermal sensing alone.

Selective combination of visual and thermal imaging for resilient localization in adverse conditions : day and night, smoke and fire

Long-term autonomy in robotics requires perception systems that are resilient to unusual but realistic conditions that will eventually occur during extended missions. For example, unmanned ground vehicles (UGVs) need to be capable of operating safely in adverse and low-visibility conditions, such as at night or in the presence of smoke. The key to a resilient UGV perception system lies in the use of multiple sensor modalities, e.g., operating at different frequencies of the electromagnetic spectrum, to compensate for the limitations of a single sensor type. In this paper, visual and infrared imaging are combined in a Visual-SLAM algorithm to achieve localization. We propose to evaluate the quality of data provided by each sensor modality prior to data combination. This evaluation is used to discard low-quality data, i.e., data most likely to induce large localization errors. In this way, perceptual failures are anticipated and mitigated. An extensive experimental evaluation is conducted on data sets collected with a UGV in a range of environments and adverse conditions, including the presence of smoke (obstructing the visual camera), fire, extreme heat (saturating the infrared camera), low-light conditions (dusk), and at night with sudden variations of artificial light. A total of 240 trajectory estimates are obtained using five different variations of data sources and data combination strategies in the localization method. In particular, the proposed approach for selective data combination is compared to methods using a single sensor type or combining both modalities without preselection. We show that the proposed framework allows for camera-based localization resilient to a large range of low-visibility conditions.

Thermal imaging study of scalar transport in shallow wakes

The thermal imaging technique relies on the usage of infrared signal to detect the temperature field. Using temperature as a flow tracer, thermography is used to investigate the scalar transport in the shallow-water wake generated by an emergent circular cylinder. Thermal imaging is demonstrated to be a good quantitative flow visualization technique for studying turbulent mixing phenomena in shallow waters. A key advantage of the thermal imaging method over other scalar measurement techniques, such as the Laser Induced Fluorescence (LIF) and Planar Concentration Analysis (PCA) methods, is that it involves a very simple experimental setup. The dispersion characteristics captured with this technique are found to be similar to past studies with traditional measurement techniques. © 2012 Publishing House for Journal of Hydrodynamics.

Infrared thermography: A non-invasive window into thermal physiology

Infrared thermography is a non-invasive technique that measures mid to long-wave infrared radiation emanating from all objects and converts this to temperature. As an imaging technique, the value of modern infrared thermography is its ability to produce a digitized image or high speed video rendering a thermal map of the scene in false colour. Since temperature is an important environmental parameter influencing animal physiology and metabolic heat production an energetically expensive process, measuring temperature and energy exchange in animals is critical to understanding physiology, especially under field conditions. As a non-contact approach, infrared thermography provides a non-invasive complement to physiological data gathering. One caveat, however, is that only surface temperatures are measured, which guides much research to those thermal events occurring at the skin and insulating regions of the body. As an imaging technique, infrared thermal imaging is also subject to certain uncertainties that require physical modeling, which is typically done via built-in software approaches. Infrared thermal imaging has enabled different insights into the comparative physiology of phenomena ranging from thermogenesis, peripheral blood flow adjustments, evaporative cooling, and to respiratory physiology. In this review, I provide background and guidelines for the use of thermal imaging, primarily aimed at field physiologists and biologists interested in thermal biology. I also discuss some of the better known approaches and discoveries revealed from using thermal imaging with the objective of encouraging more quantitative assessment.

Thermal Imaging Applications In Urban Deer Control

Control of burgeoning populations of white-tailed deer (Odocoileus virginianus) is a challenging endeavor under the best of circumstances. The challenge is further complicated when control programs are attempted within an urban or suburban area. Wildlife managers often consider management techniques and equipment which have a proven track record. New challenges require new and innovative techniques. The deer management program in Fairfax County, Virginia has employed thermal imaging technology in a variety of ways to better address these unique challenges. In addition to the more commonly used aircraft-mounted FLIR (forward looking infrared), this program utilizes vehicle-mounted and hand-held thermal imaging devices. Thermal imaging is used in determining herd densities, ensuring that control areas are free of humans, locating deer, assessing target attributes and recovering culled deer. These devices bring a higher level of safety, efficiency and efficacy to control programs operating within these difficult environs.

Thermal imaging as a biometrics approach to facial signature authentication

This dissertation develops an image processing framework with unique feature extraction and similarity measurements for human face recognition in the thermal mid-wave infrared portion of the electromagnetic spectrum. The goals of this research is to design specialized algorithms that would extract facial vasculature information, create a thermal facial signature and identify the individual. The objective is to use such findings in support of a biometrics system for human identification with a high degree of accuracy and a high degree of reliability. This last assertion is due to the minimal to no risk for potential alteration of the intrinsic physiological characteristics seen through thermal infrared imaging. The proposed thermal facial signature recognition is fully integrated and consolidates the main and critical steps of feature extraction, registration, matching through similarity measures, and validation through testing our algorithm on a database, referred to as C-X1, provided by the Computer Vision Research Laboratory at the University of Notre Dame. Feature extraction was accomplished by first registering the infrared images to a reference image using the functional MRI of the Brain’s (FMRIB’s) Linear Image Registration Tool (FLIRT) modified to suit thermal infrared images. This was followed by segmentation of the facial region using an advanced localized contouring algorithm applied on anisotropically diffused thermal images. Thermal feature extraction from facial images was attained by performing morphological operations such as opening and top-hat segmentation to yield thermal signatures for each subject. Four thermal images taken over a period of six months were used to generate thermal signatures and a thermal template for each subject, the thermal template contains only the most prevalent and consistent features. Finally a similarity measure technique was used to match signatures to templates and the Principal Component Analysis (PCA) was used to validate the results of the matching process. Thirteen subjects were used for testing the developed technique on an in-house thermal imaging system. The matching using an Euclidean-based similarity measure showed 88% accuracy in the case of skeletonized signatures and templates, we obtained 90% accuracy for anisotropically diffused signatures and templates. We also employed the Manhattan-based similarity measure and obtained an accuracy of 90.39% for skeletonized and diffused templates and signatures. It was found that an average 18.9% improvement in the similarity measure was obtained when using diffused templates. The Euclidean- and Manhattan-based similarity measure was also applied to skeletonized signatures and templates of 25 subjects in the C-X1 database. The highly accurate results obtained in the matching process along with the generalized design process clearly demonstrate the ability of the thermal infrared system to be used on other thermal imaging based systems and related databases. A novel user-initialization registration of thermal facial images has been successfully implemented. Furthermore, the novel approach at developing a thermal signature template using four images taken at various times ensured that unforeseen changes in the vasculature did not affect the biometric matching process as it relied on consistent thermal features.

A comparison between conductive and infrared devices for measuring mean skin temperature at rest, during exercise in the heat, and recovery

Purpose: Skin temperature assessment has historically been undertaken with conductive devices affixed to the skin. With the development of technology, infrared devices are increasingly utilised in the measurement of skin temperature. Therefore, our purpose was to evaluate the agreement between four skin temperature devices at rest, during exercise in the heat, and recovery. Methods: Mean skin temperature (T̅sk) was assessed in thirty healthy males during 30 min rest (24.0± 1.2°C, 56 ± 8%), 30 min cycle in the heat (38.0 ± 0.5°C, 41 ± 2%), and 45 min recovery(24.0 ± 1.3°C, 56 ± 9%). T̅sk was assessed at four sites using two conductive devices(thermistors, iButtons) and two infrared devices (infrared thermometer, infrared camera). Results: Bland–Altman plots demonstrated mean bias ± limits of agreement between the thermistors and iButtons as follows (rest, exercise, recovery): -0.01 ± 0.04, 0.26 ± 0.85, -0.37 ± 0.98°C; thermistors and infrared thermometer: 0.34 ± 0.44, -0.44 ± 1.23, -1.04 ± 1.75°C; thermistors and infrared camera (rest, recovery): 0.83 ± 0.77, 1.88 ± 1.87°C. Pairwise comparisons of T̅sk found significant differences (p < 0.05) between thermistors and both infrared devices during resting conditions, and significant differences between the thermistors and all other devices tested during exercise in the heat and recovery. Conclusions: These results indicate poor agreement between conductive and infrared devices at rest, during exercise in the heat, and subsequent recovery. Infrared devices may not be suitable for monitoring T̅sk in the presence of, or following, metabolic and environmental induced heat stress.

Autonomous UAVs wildlife detection using thermal imaging, predictive navigation and computer vision

There is an increased interest on the use of Unmanned Aerial Vehicles (UAVs) for wildlife and feral animal monitoring around the world. This paper describes a novel system which uses a predictive dynamic application that places the UAV ahead of a user, with a low cost thermal camera, a small onboard computer that identifies heat signatures of a target animal from a predetermined altitude and transmits that target’s GPS coordinates. A map is generated and various data sets and graphs are displayed using a GUI designed for easy use. The paper describes the hardware and software architecture and the probabilistic model for downward facing camera for the detection of an animal. Behavioral dynamics of target movement for the design of a Kalman filter and Markov model based prediction algorithm are used to place the UAV ahead of the user. Geometrical concepts and Haversine formula are applied to the maximum likelihood case in order to make a prediction regarding a future state of the user, thus delivering a new way point for autonomous navigation. Results show that the system is capable of autonomously locating animals from a predetermined height and generate a map showing the location of the animals ahead of the user.

Investigating limitations of SURF approach for thermal imaging analysis and mapping

Wildlife conservation involves an understanding of a specific animal, its environment and the interaction within a local ecosystem. Unmanned Aerial Vehicles (UAVs) present cost effective, non-intrusive solution for detecting animals over large areas and the use thermal imaging cameras offer the ability detect animals that would otherwise be concealed to visible light cameras. This report examines some of limitations on using SURF for the development of large maps using multiple stills images extracted from the thermal imaging video camera which contain wildlife (eg. Koala in them).