A registration error estimation framework for correlative imaging
This work addresses registration error estimation for biologists using multimodal correlative imaging, but it is incremental as it builds on existing methods.
The paper tackles the problem of estimating registration error in point-based correlative imaging by applying multivariate linear regression to compute rigid and affine transformations with anisotropic noise, providing a decision-support tool for biologists.
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.