Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Anatomy of the Eyeball

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...

You might also read

Related Articles

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

Sort by

Same author

Retinal graph neural network for segmentation of retinal vasculature and foveal avascular zone from OCTA images.

Biomedizinische Technik. Biomedical engineering·2026

Same author

Dual-Modal Deep Learning with In-Domain Training and Attention for Infant Brain Myelination Prediction.

Neuroinformatics·2026

Same author

Automated weld defect detection using gated attention and squeeze and excitation fusion U-Net.

Scientific reports·2025

Same author

Multiresolution cascaded attention U-Net for localization and segmentation of optic disc and fovea in fundus images.

Scientific reports·2024

Same author

Rainfall-Runoff modelling using SWAT and eight artificial intelligence models in the Murredu Watershed, India.

Environmental monitoring and assessment·2023

Same author

Deep learning approaches based improved light weight U-Net with attention module for optic disc segmentation.

Physical and engineering sciences in medicine·2022

Same journal

VIVIE: Virtually Integrated Ventricular Intervention Environment and its effectiveness as a teaching and learning tool.

International journal of computer assisted radiology and surgery·2026

Same journal

Contactless robotic system for linear catheter advancement using magnetic actuation.

International journal of computer assisted radiology and surgery·2026

Same journal

Sound source localization for spatial mapping of surgical actions in dynamic scenes.

International journal of computer assisted radiology and surgery·2026

Same journal

ESD-VesNet: uncertainty-aware vessel segmentation network for endoscopic submucosal dissection with hard negative mining.

International journal of computer assisted radiology and surgery·2026

Same journal

Lean Unet: a compact model for image segmentation.

International journal of computer assisted radiology and surgery·2026

Same journal

Strain alignment: toward assessing mechanical plausibility of predicted displacement fields.

International journal of computer assisted radiology and surgery·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Feb 22, 2026

Using Retinal Imaging to Study Dementia

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

PCA-based localization approach for segmentation of optic disc.

Varun P Gopi¹, M S Anjali², S Issac Niwas³

¹Department of Electronics and Communication Engineering, Government Engineering College, Wayanad, Kerala, 670644, India. varunpg@gecwyd.ac.in.

International Journal of Computer Assisted Radiology and Surgery

|October 2, 2017

Summary

This summary is machine-generated.

An efficient algorithm was developed for optic disc segmentation and detection using principal component analysis and Markov random field segmentation. This method accurately identifies the optic disc for retinal disease diagnosis.

Keywords:

Blood vessel inpainting Fundus image Markov random field Optic disc Principal component analysis

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Feb 22, 2026

Using Retinal Imaging to Study Dementia

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

Area of Science:

Ophthalmology
Medical Image Analysis
Computer Vision

Background:

The optic disc is crucial for diagnosing retinal diseases.
Accurate optic disc segmentation and detection are vital for automated eye disease identification.
Current methods require efficient algorithms for reliable diagnosis.

Purpose of the Study:

To propose an efficient algorithm for optic disc segmentation and detection.
To aid in the automated diagnosis of retinal diseases.
To improve the accuracy of optic disc identification in retinal images.

Main Methods:

Optic disc localization using principal component analysis (PCA).
Blood vessel inpainting for background image enhancement.
Optic disc segmentation employing Markov Random Field (MRF) segmentation.

Main Results:

The algorithm achieved high average overlapping scores on standard databases (MESSIDOR: 92.41%, DRIVE: 92.17%).
Validation on a local database yielded an average overlapping score of 91%.
The proposed method demonstrated superior performance compared to alternative algorithms.

Conclusions:

An efficient algorithm for optic disc detection was successfully developed.
PCA-based localization and MRF segmentation are effective for optic disc analysis.
The developed algorithm shows significant potential for clinical application in retinal disease diagnosis.