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

Anatomy of the Eyeball01:20

Anatomy of the Eyeball

The eye is a spherical, hollow structure composed of three tissue layers. The outer layer — the fibrous tunic, comprises the sclera — a white structure — and the cornea, which is transparent. The sclera encompasses some of the ocular surface, most of which is not visible. However, the 'white of the eye' is distinctively visible in humans compared to other species. The cornea, a clear covering at the front of the eye, enables light penetration. The eye's middle layer, the vascular tunic,...

You might also read

Related Articles

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

Sort by
Same author

Prominin-1 and Retinal Degenerative Disorders: Expanding the Biology from Photoreceptors to the Retinal Pigment Epithelium.

Biomolecules·2026
Same author

Prominin-1 Regulates Retinal Pigment Epithelium Homeostasis: Transcriptomic Insights into Degenerative Mechanisms.

International journal of molecular sciences·2025
Same author

Durotaxis is a driver and potential therapeutic target in lung fibrosis and metastatic pancreatic cancer.

Nature cell biology·2025
Same author

A Novel Combination Therapy Approach Targeting STAT3 and Autophagy in Glioblastoma.

Autophagy reports·2025
Same author

Prominin-1 Knockdown Causes RPE Degeneration in a Mouse Model.

Cells·2024
Same author

Pilot Safety Study of a Microfabricated Device for Anterior Stromal Puncture to Treat Corneal Epithelial Disease in the Optical Axis.

Cornea·2024

Related Experiment Video

Updated: Jul 10, 2026

In vivo Structural Assessments of Ocular Disease in Rodent Models using Optical Coherence Tomography
07:44

In vivo Structural Assessments of Ocular Disease in Rodent Models using Optical Coherence Tomography

Published on: July 24, 2020

Locating the optic nerve in retinal images: comparing model-based and Bayesian decision methods.

Thomas P Karnowski1, V Priya Govindasamy, Kenneth W Tobin

  • 1Oak Ridge Nat. Lab., Oak Ridge, TN 37831-6285, USA. karnowskitp@ornl.gov

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|October 20, 2007
PubMed
Summary

This study compares two automatic optic nerve (ON) localization methods in retinal images, developing a combined technique for improved accuracy and speed in ON detection.

More Related Videos

Optimization of the Retinal Vein Occlusion Mouse Model to Limit Variability
07:23

Optimization of the Retinal Vein Occlusion Mouse Model to Limit Variability

Published on: August 6, 2021

Topographical Estimation of Visual Population Receptive Fields by fMRI
06:02

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

Related Experiment Videos

Last Updated: Jul 10, 2026

In vivo Structural Assessments of Ocular Disease in Rodent Models using Optical Coherence Tomography
07:44

In vivo Structural Assessments of Ocular Disease in Rodent Models using Optical Coherence Tomography

Published on: July 24, 2020

Optimization of the Retinal Vein Occlusion Mouse Model to Limit Variability
07:23

Optimization of the Retinal Vein Occlusion Mouse Model to Limit Variability

Published on: August 6, 2021

Topographical Estimation of Visual Population Receptive Fields by fMRI
06:02

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Computer Vision

Background:

  • Accurate optic nerve (ON) localization is crucial for diagnosing various eye conditions.
  • Current automated methods for ON localization in retinal imagery have limitations in accuracy and efficiency.

Purpose of the Study:

  • To compare two distinct methods for automatic optic nerve (ON) localization in retinal images.
  • To enhance a model-based technique by integrating linear discriminant analysis and Bayesian decision theory.
  • To develop and evaluate a composite technique combining both approaches for superior performance.

Main Methods:

  • A Bayesian decision theory discriminator utilizing four spatial features of retinal imagery.
  • A principal component-based reconstruction model for optic nerve (ON) representation.
  • Integration of linear discriminant analysis and Bayesian decision theory into the model-based technique.
  • Development of a composite method merging the strengths of both approaches.

Main Results:

  • The study evaluated two primary methods for automatic optic nerve (ON) localization.
  • An improved model-based technique incorporating linear discriminant analysis and Bayesian decision theory was developed.
  • A composite technique was explored, demonstrating high accuracy and rapid throughput.
  • Performance was validated on a dataset of 395 retinal images using 2-fold cross-validation.

Conclusions:

  • The developed composite technique offers a promising solution for accurate and efficient automatic optic nerve (ON) localization.
  • Combining different methodological approaches can lead to significant improvements in retinal image analysis.
  • Further validation and application of this composite method could enhance clinical diagnostic capabilities.