An automated approach to enhance the thermographic evaluation on orofacial regions in lateral facial thermograms.

Segmentation of characteristic facial regions has often been an initial step of thermographic analysis in face recognition and clinical diagnosis. Moreover, fast and accurate thermographic analysis on characteristic areas is highly reliant on segmentation approach. Usually, frontal and lateral projections are used to capture the facial thermograms. The significant role of lateral facial thermography to diagnose the various problems associated with orofacial regions has been remarkable in many studies. So far, no study has presented an automatic approach for the segmentation of characteristic areas in lateral facial thermograms. For this purpose, an automatic approach to segment the characteristic areas in lateral facial thermograms is proposed. The dataset of 140 facial thermograms with 1 left and 1 right lateral view per subject is created. Initially, image binarization is performed using optimal temperature thresholding for better visualization of facial geometry. Then, the automatic localization of characteristic points is performed at two levels, based on (a) geometrical features of the face, and (b) local thermal patterns. At last, the characteristic points and auxiliary points are used to segment the characteristic areas in the orofacial region of the face. To evaluate the segmentation performance of the proposed methodology, the automatically localized characteristic points are compared with manually marked using Euclidean distance based comparison criterion. With the localization error δch_pt ≤0.05, the proposed automatic approach shows 92.04% of overall localization accuracy and 85% of overall segmentation accuracy. The key conclusion is that the proposed algorithm can potentially automate the process of thermographic analysis on characteristic areas to assist the diagnosis of problems in the orofacial region.