A Generative Approach for Image Registration of Visible-Thermal (VT)
  Cancer Faces
Catherine Ordun Alexandra Cha Edward Raff Sanjay Purushotham Karen Kwok Mason Rule James Gulley
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.