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

Flow geometry effect on Pseudomonas fluorescens SBW25 biofilm structure.

Colloids and surfaces. B, Biointerfaces·2025
Same author

Large Scale Optical Projection Tomography without the Use of Refractive-Index-Matching Liquid.

Sensors (Basel, Switzerland)·2023
Same author

Highly sensitive resistance spectroscopy technique for online monitoring of biofilm growth on metallic surfaces.

Environmental research·2023
Same author

Relationship of 24-h ambulatory blood pressure variability with micro and macrovascular parameters and hypertension status.

Journal of hypertension·2022
Same author

Monitoring Health Parameters of Elders to Support Independent Living and Improve Their Quality of Life.

Sensors (Basel, Switzerland)·2021
Same author

Retinal image registration as a tool for supporting clinical applications.

Computer methods and programs in biomedicine·2020

Related Experiment Video

Updated: Mar 27, 2026

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

8.3K

Retinal image registration based on keypoint correspondences, spherical eye modeling and camera pose estimation.

Carlos Hernandez-Matas, Xenophon Zabulis, Antonis A Argyros

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 7, 2016
    PubMed
    Summary

    This study introduces a novel retinal image registration method using keypoint correspondences and a spherical eye model. The technique accurately aligns images, improving diagnostic capabilities and analysis for various eye examinations.

    More Related Videos

    Video-oculography in Mice
    09:43

    Video-oculography in Mice

    Published on: July 19, 2012

    24.6K
    Using Eye-tracking to Assess the Relative Importance of Visual and Vestibular Input to Subcortical Motion Processing in the Roll Plane
    07:24

    Using Eye-tracking to Assess the Relative Importance of Visual and Vestibular Input to Subcortical Motion Processing in the Roll Plane

    Published on: August 22, 2025

    637

    Related Experiment Videos

    Last Updated: Mar 27, 2026

    A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
    12:39

    A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

    Published on: January 18, 2020

    8.3K
    Video-oculography in Mice
    09:43

    Video-oculography in Mice

    Published on: July 19, 2012

    24.6K
    Using Eye-tracking to Assess the Relative Importance of Visual and Vestibular Input to Subcortical Motion Processing in the Roll Plane
    07:24

    Using Eye-tracking to Assess the Relative Importance of Visual and Vestibular Input to Subcortical Motion Processing in the Roll Plane

    Published on: August 22, 2025

    637

    Area of Science:

    • Ophthalmology
    • Medical Imaging
    • Computer Vision

    Background:

    • Accurate alignment of retinal images is crucial for diagnosing and monitoring eye conditions.
    • Existing image registration methods face challenges with noise and variations in imaging conditions.

    Purpose of the Study:

    • To develop an accurate and robust image registration method for retinal images.
    • To treat retinal image registration as a pose estimation problem for improved alignment.

    Main Methods:

    • A novel image registration method utilizing keypoint correspondences.
    • Assumption of a spherical model of the eye for geometric transformation.
    • Pose estimation to determine the rigid transformation between two retinal images.

    Main Results:

    • The proposed method demonstrates improved accuracy compared to state-of-the-art approaches.
    • The method exhibits robustness against noise and spurious keypoint correspondences.
    • Successful application in diagnostic image enhancement and comparative analysis.

    Conclusions:

    • The developed retinal image registration technique offers enhanced accuracy and robustness.
    • The method is suitable for clinical applications, including image enhancement and comparative analysis.
    • This approach advances the field of medical image analysis in ophthalmology.