Facial thermography: a potential complimentary tool for evaluation of dental disorders

Abstract

The aim and objectives of the study was to analyze the skin surface temperature distribution in dental disorders using thermal imaging and to segment the region of interest using k-means algorithm and to perform statistical feature extraction for the total population studied. Thermal images were obtained for left profile, right profile, and front profile for the total population to be studied. The thermal image was segmented using k-means algorithm and the features were extracted from the segmented output image for both normal and dental disorder subjects. The results obtained from the study depicts that the mean temperature difference between the abnormal diseased subjects and the healthy controls in the front, left and right facial region was found to be 8.75%, 7.89% and 9.3% respectively. Among the regions examined in the facial thermogram, right facial region depicts the highest percentage difference of 9.3% found between the diseased subjects and normal group. From this data it is safe to infer that, presence of infections, significantly increases the temperature of the region. Hence thermography was used as a complimentary diagnostic tool in diagnosis of dental disorders.

Automated detection of orofacial pain from thermograms using machine learning and deep learning approaches

“…In our earlier studies, we have performed automated segmentation in thermal images of OFP and extracted the features from the segmented images (Mir et al, 2019). We have demonstrated facial thermogram as a complementary tool in the assessment of OFP in our previous works (Snekhalatha et al, 2018).…”