A Generative Approach for Image Registration of Visible-Thermal (VT) Cancer Faces
This work addresses a domain-specific problem for AI-based pain research in cancer patients, offering an incremental improvement by modernizing image registration with generative methods.
The paper tackles misalignment between visible and thermal facial images of cancer patients by applying a generative alignment algorithm for image registration, resulting in a 52.5% improvement in thermal image quality for visible-to-thermal translation tasks.
Since thermal imagery offers a unique modality to investigate pain, the U.S. National Institutes of Health (NIH) has collected a large and diverse set of cancer patient facial thermograms for AI-based pain research. However, differing angles from camera capture between thermal and visible sensors has led to misalignment between Visible-Thermal (VT) images. We modernize the classic computer vision task of image registration by applying and modifying a generative alignment algorithm to register VT cancer faces, without the need for a reference or alignment parameters. By registering VT faces, we demonstrate that the quality of thermal images produced in the generative AI downstream task of Visible-to-Thermal (V2T) image translation significantly improves up to 52.5\%, than without registration. Images in this paper have been approved by the NIH NCI for public dissemination.