Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Cramér-Rao Lower Bound for Point Based Image Registration With Heteroscedastic Error Model for Application in Single Molecule Microscopy.

IEEE transactions on medical imaging·2015
Same author

Measurement Errors in Fluorescence Microscopy Image Registration.

Conference record. Asilomar Conference on Signals, Systems & Computers·2015
Same author

Analysis of Point Based Image Registration Errors With Applications in Single Molecule Microscopy.

IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society·2014
Same author

Noise suppression of point spread functions and its influence on deconvolution of three-dimensional fluorescence microscopy image sets.

Journal of microscopy·2005
Same author

Differences in promiscuity for antibody-FcRn interactions across species: implications for therapeutic antibodies.

International immunology·2001
Same author

The MHC class I-related receptor, FcRn, plays an essential role in the maternofetal transfer of gamma-globulin in humans.

International immunology·2001

Related Experiment Video

Updated: Apr 8, 2026

Single-Molecule Localization Microscopy of Membrane Proteins using Single-Antibody Labeling
07:51

Single-Molecule Localization Microscopy of Membrane Proteins using Single-Antibody Labeling

Published on: March 20, 2026

235

IMAGE REGISTRATION ERROR ANALYSIS WITH APPLICATIONS IN SINGLE MOLECULE MICROSCOPY.

E A K Cohen1, R J Ober1

  • 1Eric Jonsson School of Electrical Engineering and Computer Science, University of Texas at Dallas, Richardson, TX 75083-0688 USA.

Proceedings. IEEE International Symposium on Biomedical Imaging
|June 30, 2015
PubMed
Summary
This summary is machine-generated.

This study addresses localization errors in fluorescence microscopy image registration. Generalized least squares, accounting for measurement errors, provides a more accurate method than traditional linear least squares for precise image analysis.

Keywords:
Image registrationMicroscopyTotal least squares methods

More Related Videos

An In Vitro Single-Molecule Imaging Assay for the Analysis of Cap-Dependent Translation Kinetics
09:52

An In Vitro Single-Molecule Imaging Assay for the Analysis of Cap-Dependent Translation Kinetics

Published on: September 15, 2020

3.5K
Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells
11:06

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells

Published on: June 30, 2018

9.2K

Related Experiment Videos

Last Updated: Apr 8, 2026

Single-Molecule Localization Microscopy of Membrane Proteins using Single-Antibody Labeling
07:51

Single-Molecule Localization Microscopy of Membrane Proteins using Single-Antibody Labeling

Published on: March 20, 2026

235
An In Vitro Single-Molecule Imaging Assay for the Analysis of Cap-Dependent Translation Kinetics
09:52

An In Vitro Single-Molecule Imaging Assay for the Analysis of Cap-Dependent Translation Kinetics

Published on: September 15, 2020

3.5K
Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells
11:06

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells

Published on: June 30, 2018

9.2K

Area of Science:

  • Microscopy
  • Image Analysis
  • Biophysics

Background:

  • Accurate localization is crucial in fluorescence microscopy.
  • Image registration aligns multiple images for analysis.
  • Traditional methods struggle with inherent measurement errors.

Purpose of the Study:

  • To assess localization errors in monochromatic fluorescence image registration.
  • To identify appropriate statistical methods for handling errors-in-variables in image registration.
  • To develop a framework for deriving localization errors based on experimental parameters.

Main Methods:

  • Framing image registration as an errors-in-variables problem.
  • Applying multivariate generalized least squares (GLS) for accurate regression.
  • Utilizing an extension of GLS to accommodate non-independent and identically distributed (non-iid) noise.

Main Results:

  • Demonstrated that linear least squares is inappropriate due to measurement errors.
  • Established generalized least squares as the correct approach for this problem.
  • Derived localization errors based on photon counts and experimental parameters using advanced GLS techniques.

Conclusions:

  • Accurate image registration in fluorescence microscopy requires advanced statistical methods.
  • The proposed generalized least squares approach minimizes localization errors.
  • Understanding these errors is key to improving quantitative analysis in microscopy.