2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Login Paper Search My Schedule Paper Index Help

My ICIP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDBIO-3.4
Paper Title A REGISTRATION ERROR ESTIMATION FRAMEWORK FOR CORRELATIVE IMAGING
Authors Guillaume Potier, Université de Nantes, CNRS, INSERM, France; Frédéric Lavancier, Université de Nantes, France; Stephan Kunne, Université de Nantes, CNRS, INSERM, France; Perrine Paul-Gilloteaux, Université de Nantes, Inserm, CNRS, France
SessionBIO-3: Biomedical Signal Processing 3
LocationArea C
Session Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Poster
Topic Biomedical Signal Processing: Biological image analysis
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract 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 an original 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.