A Generative Approach for Image Registration of Visible-Thermal (VT) Cancer Faces
Abstract
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.
Keywords
Cite
@article{arxiv.2308.12271,
title = {A Generative Approach for Image Registration of Visible-Thermal (VT) Cancer Faces},
author = {Catherine Ordun and Alexandra Cha and Edward Raff and Sanjay Purushotham and Karen Kwok and Mason Rule and James Gulley},
journal= {arXiv preprint arXiv:2308.12271},
year = {2023}
}
Comments
2nd Annual Artificial Intelligence over Infrared Images for Medical Applications Workshop (AIIIMA) at the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023)