An application based on the analysis of thermograms to calculate the thermographic index for pain and swelling assessment.

As the world's population ages, the prevalence of rheumatic diseases is increasing. These diseases are often diagnosed and monitored using imaging techniques such as infrared thermography. In this study, we developed an application to calculate the thermographic index (TI) in a series of thermograms. Such index is used in rheumatology as a complementary diagnostic tool to assess pain and swelling. We designed the application in MATLAB App Designer; an environment for creating applications with a graphical user interface. To evaluate the performance of the developed application, we calculated the TI of ten trigger points (anterior shoulder, sternum, posterior shoulder, upper back, lower back, elbow, lateral shoulder, elbow pit, knee, and back of the knee) in a series of thermal images from 37 healthy participants. Results of the average temperature varied between 31.35 (knee for women) and 33.92 (lateral shoulder for men) in °C and TIs were lower than 2 and the results in the knee region are consistent with those reported in previous studies due to only the TI in the knee is known. This study contributes to the literature with reference values for nine trigger points other than the knee. This app is easy to use and may be useful in future applications for disease detection or monitoring of its progression.

Automated hand thermal image segmentation and feature extraction in the evaluation of rheumatoid arthritis.

The aim of the study was (1) to perform an automated segmentation of hot spot regions of the hand from thermograph using the k-means algorithm and (2) to test the potential of features extracted from the hand thermograph and its measured skin temperature indices in the evaluation of rheumatoid arthritis. Thermal image analysis based on skin temperature measurement, heat distribution index and thermographic index was analyzed in rheumatoid arthritis patients and controls. The k-means algorithm was used for image segmentation, and features were extracted from the segmented output image using the gray-level co-occurrence matrix method. In metacarpo-phalangeal, proximal inter-phalangeal and distal inter-phalangeal regions, the calculated percentage difference in the mean values of skin temperatures was found to be higher in rheumatoid arthritis patients (5.3%, 4.9% and 4.8% in MCP3, PIP3 and DIP3 joints, respectively) as compared to the normal group. k-Means algorithm applied in the thermal imaging provided better segmentation results in evaluating the disease. In the total population studied, the measured mean average skin temperature of the MCP3 joint was highly correlated with most of the extracted features of the hand. In the total population studied, the statistical feature extracted parameters correlated significantly with skin surface temperature measurements and measured temperature indices. Hence, the developed computer-aided diagnostic tool using MATLAB could be used as a reliable method in diagnosing and analyzing the arthritis in hand thermal images.