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A Registration Error Estimation Framework for Correlative Imaging

Abstract : Correlative imaging workflows are now widely used in bio-imaging 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.
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Contributor : Perrine Paul-Gilloteaux Connect in order to contact the contributor
Submitted on : Friday, August 27, 2021 - 11:44:30 AM
Last modification on : Thursday, September 1, 2022 - 11:11:55 AM

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Guillaume Potier, Frederic Lavancier, Stephan Kunne, Perrine Paul-Gilloteaux. A Registration Error Estimation Framework for Correlative Imaging. 2021 IEEE International Conference on Image Processing (ICIP), Sep 2021, Anchorage, United States. pp.131-135, ⟨10.1109/ICIP42928.2021.9506474⟩. ⟨hal-03327500⟩



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