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

Catherine Ordun, Alexandra Cha, Edward Raff, Sanjay Purushotham, Karen Kwok +2 more
8/23/2023
cs.CV

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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.

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Cite this paper

@article{ordun2023generative,
  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},
  year   = {2023},
  eprint = {2308.12271},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2308.12271v1}
}

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