Registration of long-term recordings of thermographic video applied to foot temperature monitoring

Abstract

Dynamic thermal imaging of human subjects presents unique challenges to automated data processing. Variations in background-foreground contrast and diverse patterns on regions of interest of the body mean that classical processing techniques which were developed for RGB images might not be suitable for this kind of data. Additionally, subject movement during recording complicates the process further and necessitates correction for accurate thermal video analysis. In this study, a method for registering thermal video data is presented, allowing each pixel to correspond to the same anatomical location throughout the video. This registration facilitates subsequent processing, such as ROI extraction. The proposed registration method has two steps: the first addresses large linear deformations, while the second uses deep learning based on the SynthMorph architecture to register smaller, elastic deformations. This method manages to reduce the mean displacement of salient points by 71.5% on our test dataset. The algorithm was tested on thermal video data of the plantar aspect of human feet but has the potential to be implemented on other greyscale images and in other medical applications.