A Generative Approach for Image Registration of Visible-Thermal (VT) Cancer Faces

TL;DR

This paper introduces a generative image registration method for aligning visible and thermal cancer face images, significantly improving thermal image quality for AI-based pain analysis.

Contribution

It proposes a novel generative alignment algorithm for VT image registration that does not require reference images or parameters, enhancing downstream thermal image translation.

Findings

Thermal image quality improved by up to 52.5% after registration.

The method effectively aligns VT images without reference or prior parameters.

Improved registration enhances AI-based thermal image analysis.

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.