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

Unified extractive-abstractive summarization: a hybrid approach utilizing BERT and transformer models for enhanced document summarization.

PeerJ. Computer science·2025
Same author

GIT-Net: An Ensemble Deep Learning-Based GI Tract Classification of Endoscopic Images.

Bioengineering (Basel, Switzerland)·2023
Same author

Small-Scale and Occluded Pedestrian Detection Using Multi Mapping Feature Extraction Function and Modified Soft-NMS.

Computational intelligence and neuroscience·2022
Same author

Combining Latent Factor Model for Dynamic Recommendations in Community Question Answering Forums.

Computational intelligence and neuroscience·2022
Same author

The Rise of Cloud Computing: Data Protection, Privacy, and Open Research Challenges-A Systematic Literature Review (SLR).

Computational intelligence and neuroscience·2022
Same author

Attention Autoencoder for Generative Latent Representational Learning in Anomaly Detection.

Sensors (Basel, Switzerland)·2022

Related Experiment Video

Updated: Nov 23, 2025

Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography
07:23

Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography

Published on: March 26, 2020

8.5K

A Hybrid Unsupervised Approach for Retinal Vessel Segmentation.

Khan Bahadar Khan1, Muhammad Shahbaz Siddique2, Muhammad Ahmad3

  • 1Department of Telecommunication Engineering, Faculty of Engineering, The Islamia University of Bahawalpur, Bahawalpur, Pakistan.

Biomed Research International
|December 31, 2020
PubMed
Summary

This study introduces a new framework for fast and accurate retinal vessel segmentation (RVS) to aid in diagnosing eye diseases. The method enhances vessel structures for improved detection in ophthalmic imaging.

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

3.1K
Quantification of Vascular Parameters in Whole Mount Retinas of Mice with Non-Proliferative and Proliferative Retinopathies
12:28

Quantification of Vascular Parameters in Whole Mount Retinas of Mice with Non-Proliferative and Proliferative Retinopathies

Published on: March 12, 2022

4.0K

Related Experiment Videos

Last Updated: Nov 23, 2025

Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography
07:23

Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography

Published on: March 26, 2020

8.5K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

3.1K
Quantification of Vascular Parameters in Whole Mount Retinas of Mice with Non-Proliferative and Proliferative Retinopathies
12:28

Quantification of Vascular Parameters in Whole Mount Retinas of Mice with Non-Proliferative and Proliferative Retinopathies

Published on: March 12, 2022

4.0K

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Computer Vision

Background:

  • Retinal vessel segmentation (RVS) is crucial for diagnosing ophthalmic disorders like diabetic retinopathy and stroke.
  • Current automatic RVS systems face challenges in accuracy and speed.
  • The retinal vascular network's structure is vital for identifying various systemic and ocular diseases.

Purpose of the Study:

  • To propose an efficient and effective framework for automated retinal vessel segmentation.
  • To improve the accuracy of identifying the retinal vascular network for clinical applications.

Main Methods:

  • A novel framework combining MISODATA and B-COSFIRE filters for vessel enhancement.
  • Utilizing logical AND operations and image fusion to generate a vessel location map (VLM).
  • Preprocessing, binarization, enhancement, and fusion techniques applied to retinal images.

Main Results:

  • The proposed framework achieves fast execution and competitive results on multiple public datasets (DRIVE, STARE, HRF, CHASE_DB1).
  • The method demonstrates effective enhancement of vessel structures for accurate segmentation.
  • Performance was validated against existing state-of-the-art RVS methods.

Conclusions:

  • The developed framework offers a promising solution for automated RVS.
  • This approach can significantly aid in the early detection and monitoring of ophthalmic and systemic diseases.
  • The validated results suggest potential for clinical integration in ophthalmic diagnostics.