A registration error estimation framework for correlative imaging

Guillaume Potier; Frédéric Lavancier; Stephan Kunne; Perrine Paul-Gilloteaux

doi:10.1109/ICIP42928.2021.9506474

A registration error estimation framework for correlative imaging

Quantitative Methods 2021-08-30 v1 Computer Vision and Pattern Recognition

Authors: Guillaume Potier , Frédéric Lavancier , Stephan Kunne , Perrine Paul-Gilloteaux

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Abstract

Correlative imaging workflows are now widely used in bioimaging and aims to image the same sample using at least two different and complementary imaging modalities. Part of the workflow relies on finding the transformation linking a source image to a target image. We are specifically interested in the estimation of registration error in point-based registration. We propose an application of multivariate linear regression to solve the registration problem allowing us to propose a framework for the estimation of the associated error in the case of rigid and affine transformations and with anisotropic noise. These developments can be used as a decision-support tool for the biologist to analyze multimodal correlative images and are available under Ec-CLEM, an open-source plugin under ICY.

Keywords

biomedical image analysis computational biology software and tools machine learning in genomics

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@article{arxiv.2103.06256,
  title  = {A registration error estimation framework for correlative imaging},
  author = {Guillaume Potier and Frédéric Lavancier and Stephan Kunne and Perrine Paul-Gilloteaux},
  journal= {arXiv preprint arXiv:2103.06256},
  year   = {2021}
}

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10 pages 2 figures (made of 10 panels in total)

A registration error estimation framework for correlative imaging

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