Automated Region Extraction from Thermal Images for Peripheral Vascular Disease Monitoring

Abstract

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.

Automatic segmentation of plantar thermograms using adaptive C means technique

“…This algorithm worked perfectly for all the current images when compared with the ground result. Generally, it will be possible to make an initial decision based on the visual inspection [28] to verify if the segmentation corresponding to an ROI was extracted correctly or not. In this inspection, it was found that the proposed algorithm, perfectly does the background-foreground segmentation.…”

Quantitative Analysis of Real-Time Infrared Thermography for the Assessment of Lumbar Sympathetic Blocks: A Preliminary Study

“…Each plantar foot was divided into 11 regions of interest (ROIs) [55,71] shown in Fig- ure 3b: ROIs 1 to 5 are the toes, ROIs 6 to 8 are the metatarsal areas of the foot and finally ROIs 9 to 11 are the ones situated on the heel of the foot. For each one of these regions, the mean temperature, the standard deviation (SD) and the maximum value were extracted Measurements were made at the affected and contralateral extremities at baseline (right after the injection of the local anesthetic) and 10 min after the injection of the medication.…”

Screening and Diagnosis of Peripheral Arterial Disease in Patients with Diabetes: Current Perspectives

“…Screening is the key to detecting early-stage disease and allows the initiation of optimal preventive medical treatment, which may reduce modifiable risk factors for patients at risk for arteriosclerotic disease [43]. An emerging modality that in the future could have an impact on detection of PAD is medical thermography, which has been shown to detect higher forefoot temperatures associated with this condition in type 2 diabetes mellitus [44,45].…”