A Registration Error Estimation Framework for Correlative Imaging

Guillaume Potier (1) , Frederic Lavancier (2) , Stephan Kunne (1) , Perrine Paul-Gilloteaux (3, 1)

1 ITX - ITX - unité de recherche de l'institut du thorax
2 LMJL - Laboratoire de Mathématiques Jean Leray
3 SFR François Bonamy - Structure fédérative de recherche François Bonamy

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.

Domaines: Traitement des images [eess.IV]; Sciences du Vivant [q-bio]; Applications [stat.AP]; Imagerie

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⟩