A Registration Error Estimation Framework for Correlative Imaging - Nantes Université Access content directly
Conference Papers Year : 2021

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.
Fichier principal
Vignette du fichier
210306256.pdf (1.25 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03327500 , version 1 (19-12-2023)

Identifiers

Cite

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⟩
98 View
3 Download

Altmetric

Share

Gmail Facebook X LinkedIn More