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

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2308.12271.pdf (7.44 MB)

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https://arxiv.org/abs/2308.12271

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https://doi.org/10.48550/arXiv.2308.12271
 http://hdl.handle.net/11603/29659

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UMBC Information Systems Department
UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection

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https://orcid.org/0000-0002-9900-1972

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2023-08-23

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conference papers and proceedings
preprints
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This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
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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.