The Application of Medical Thermography to Discriminate Neuroischemic Toe Ulceration in the Diabetic Foot.

This study aimed to determine whether thermal imaging can detect temperature differences between healthy feet, nonulcerated neuroischemic feet, and neuroischemic feet with toe ulcers in patients with type 2 diabetes mellitus (T2DM). Participants were prospectively divided into 3 groups: T2DM without foot problems; a healthy, nonulcerated neuroischemic group, and an ulcerated neuroischemic group. Thermal images of the feet were obtained with automated segmentation of regions of interest. Thermographic images from 43 neuroischemic feet, 21 healthy feet, and 12 neuroischemic feet with active ulcer in one of the toes were analyzed. There was a significant difference in toe temperatures between the 3 groups ( P = .001), that is, nonulcerated neuroischemic (n = 181; mean temperature = 27.7°C [±2.16 SD]) versus neuroischemic ulcerated (n = 12; mean temperature = 28.7°C [±3.23 SD]), and healthy T2DM group (n = 104; mean temperature = 24.9°C [±5.04 SD]). A post hoc analysis showed a significant difference in toe temperatures between neuroischemic nonulcerated and healthy T2DM groups ( P = .001), neuroischemic ulcerated and healthy groups ( P = .001). However, no significant differences in toe temperatures were identified between the ulcerated neuroischemic and nonulcerated neuroischemic groups ( P = .626). There were no significant differences between the ulcerated toes (n = 12) and the nonulcerated toes (n = 57) of the same foot in the ulcerated neuroischemic group ( P = .331). Toe temperatures were significantly higher in neuroischemic feet with or without ulceration compared with healthy feet in patients with T2DM. There were no significant differences in temperatures of ulcerated toes and the nonulcerated toes of the same foot, implying that all the toes of the same foot could potentially be at risk of developing complications, which can be potentially detected by infrared thermography.

Establishing Differences in Thermographic Patterns between the Various Complications in Diabetic Foot Disease.

AIM: To evaluate the potential of thermography as an assessment tool for the detection of foot complications by understanding the variations in temperature that occur in type 2 diabetes mellitus (DM). METHODS: Participants were categorized according to a medical examination, ankle brachial index, doppler waveform analysis, and 10-gram monofilament testing into five groups: healthy adult, DM with no complications, DM with peripheral neuropathy, DM with neuroischaemia, and DM with peripheral arterial disease (PAD) groups. Thermographic imaging of the toes and forefeet was performed. RESULTS: 43 neuroischaemic feet, 41 neuropathic feet, 58 PAD feet, 21 DM feet without complications, and 126 healthy feet were analyzed. The temperatures of the feet and toes were significantly higher in the complications group when compared to the healthy adult and DM healthy groups. The higher the temperatures of the foot in DM, the higher the probability that it is affected by neuropathy, neuroischaemia, or PAD. CONCLUSIONS: Significant differences in mean temperatures exist between participants who were healthy and those with DM with no known complications when compared to participants with neuroischaemia, neuropathy, or PAD. As foot temperature rises, so does the probability of the presence of complications of neuropathy, neuroischaemia, or peripheral arterial disease.

The identification of higher forefoot temperatures associated with peripheral arterial disease in type 2 diabetes mellitus as detected by thermography.

AIMS: The purpose of this study was to investigate whether heat emitted from the feet of patients with type 2 diabetes (DM) and peripheral arterial disease (PAD) differed from those with type 2 diabetes without complications (DM). METHODS: A non-experimental, comparative prospective study design was employed in a tertiary referral hospital. Out of 223 randomly selected participants (430 limbs) who were initially tested, 62 limbs were categorized as DM+PAD and 22 limbs as DM without PAD. Subjects with evidence of peripheral neuropathy were excluded. Participants underwent thermographic imaging. Automatic segmentation of regions of interest extracted the temperature data. RESULTS: A significant difference in temperature in all the toes between the two groups was found (p=0.005, p=0.033, p=0.015, p=0.038 and p=0.02 for toes 1-5 respectively). The mean forefoot temperature in DM+PAD was significantly higher than that in DM (p=.019), with DM+PAD having a higher mean temperature (28.3°C) compared to DM (26.2°C). Similarly, the toes of subjects with DM+PAD were significantly warmer than those of subjects with DM only. CONCLUSIONS: Contrary to expectations the mean toe and forefoot temperatures in DM patients with PAD is higher than in those with DM only. This unexpected result could be attributed to disruption of noradrenergic vasoconstrictor thermoregulatory mechanisms with resulting increased flow through cutaneous vessels and subsequent increased heat emissivity. These results demonstrate that thermography may have potential in detecting PAD and associated temperature differences.

Automated Region Extraction from Thermal Images for Peripheral Vascular Disease Monitoring.

This work develops a method for automatically extracting temperature data from prespecified anatomical regions of interest from thermal images of human hands, feet, and shins for the monitoring of peripheral arterial disease in diabetic patients. Binarisation, morphological operations, and geometric transformations are applied in cascade to automatically extract the required data from 44 predefined regions of interest. The implemented algorithms for region extraction were tested on data from 395 participants. A correct extraction in around 90% of the images was achieved. The process of automatically extracting 44 regions of interest was performed in a total computation time of approximately 1 minute, a substantial improvement over 10 minutes it took for a corresponding manual extraction of the regions by a trained individual. Interrater reliability tests showed that the automatically extracted ROIs are similar to those extracted by humans with minimal temperature difference. This set of algorithms provides a sufficiently accurate and reliable method for temperature extraction from thermal images at par with human raters with a tenfold reduction in time requirement. The automated process may replace the manual human extraction, leading to a faster process, making it feasible to carry out large-scale studies and to increase the regions of interest with minimal cost. The code for the developed algorithms, to extract the 44 ROIs from thermal images of hands, feet, and shins, has been made available online in the form of MATLAB functions and can be accessed from http://www.um.edu.mt/cbc/tipmid.

Automated segmentation of regions of interest from thermal images of hands.

Thermal imaging can provide an image of the surface temperature of an object in a non-contact and noninvasive manner, making it particularly appealing for use in medical applications. In applications where it is desirable to extract temperature data from anatomical regions of interest (ROIs) in a standardised and consistent manner, the use of automated segmentation and analysis techniques can provide a faster, more reliable and more consistent approach than manual segmentation of these ROIs. In this paper we present an algorithm which automatically extracts temperature data from eight ROIs in thermal images of the volar aspect of human hands. The algorithm first identifies the hand from the background in the thermal image and then identifies pixels which make up the fingers and the palm. Finally, eight ROIs are extracted from the identified regions. The methods proposed in this work can also be extended for the processing of similar visual images.