A registration error estimation framework for correlative imaging

by   Guillaume Potier, et al.

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.



There are no comments yet.


page 10


Deep Convolutional Neural Network for Non-rigid Image Registration

Images taken at different times or positions undergo transformations suc...

Non-rigid Registration Method between 3D CT Liver Data and 2D Ultrasonic Images based on Demons Model

The non-rigid registration between CT data and ultrasonic images of live...

Fast and Robust Symmetric Image Registration Based on Intensity and Spatial Information

Intensity-based image registration approaches rely on similarity measure...

Real-time multimodal image registration with partial intraoperative point-set data

We present Free Point Transformer (FPT) - a deep neural network architec...

Multimodality Biomedical Image Registration using Free Point Transformer Networks

We describe a point-set registration algorithm based on a novel free poi...

An Elastic Image Registration Approach for Wireless Capsule Endoscope Localization

Wireless Capsule Endoscope (WCE) is an innovative imaging device that pe...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.