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 Experiment Video

Updated: Jun 19, 2026

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

Multi-scale retinal vessel segmentation using line tracking.

Marios Vlachos1, Evangelos Dermatas

  • 1Department of Electrical Engineering and Computer Technology, University of Patras, Patras, Greece. mvlachos@george.wcl2.ee.upatras.gr

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|November 7, 2009
PubMed
Summary
This summary is machine-generated.

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

A Robust End-to-End IoT System for Supporting Workers in Mining Industries.

Sensors (Basel, Switzerland)·2024
Same author

Behavior policy learning: Learning multi-stage tasks <i>via</i> solution sketches and model-based controllers.

Frontiers in robotics and AI·2022
Same author

Finger Vein Segmentation from Infrared Images Based on a Modified Separable Mumford Shah Model and Local Entropy Thresholding.

Computational and mathematical methods in medicine·2015
Same journal

RGCNN-nnUNet: Recurrent group equivariant nnU-Net for robust brain tissue segmentation on stroke NCCT.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same journal

Self-supervised isotropic reconstruction for abnormality detection in anisotropic MRI.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same journal

WDBDM: Wavelet-based dual-branch diffusion model for low-dose CT and PET denoising.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same journal

ScribSAM: A robust scribble-supervised framework for spatiotemporal segmentation of breast lesions in ultrasound videos.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same journal

Anatomically and biochemically guided deep image prior for sodium MRI denoising.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same journal

Segment Anything Model for medical image segmentation: A review.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
See all related articles

This study introduces a novel algorithm for retinal vessel segmentation and network extraction. The method achieves high accuracy and robustness, closely matching manual segmentation and outperforming other techniques in noisy conditions.

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Computer Vision

Background:

  • Accurate segmentation of retinal blood vessels is crucial for diagnosing various eye diseases.
  • Existing methods often struggle with noise and maintaining vessel connectivity.

Purpose of the Study:

  • To propose a novel algorithm for robust retinal vessel segmentation and network extraction.
  • To evaluate the algorithm's performance against manual segmentation and other state-of-the-art methods.

Main Methods:

  • A multi-scale line-tracking procedure initiated from selected pixels.
  • Combining multi-scale confidence maps and applying median filtering for network refinement.
  • Post-processing using directional attributes and morphological reconstruction to remove errors.

More Related Videos

Measuring Retinal Vessel Diameter from Mouse Fluorescent Angiography Images
04:04

Measuring Retinal Vessel Diameter from Mouse Fluorescent Angiography Images

Published on: May 19, 2023

Related Experiment Videos

Last Updated: Jun 19, 2026

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

Measuring Retinal Vessel Diameter from Mouse Fluorescent Angiography Images
04:04

Measuring Retinal Vessel Diameter from Mouse Fluorescent Angiography Images

Published on: May 19, 2023

Main Results:

  • Achieved an average accuracy of 0.929, sensitivity of 0.747, and specificity of 0.955 on the DRIVE database.
  • Demonstrated superior average sensitivity compared to supervised and unsupervised methods at similar accuracy and specificity.
  • Exhibited robustness against Salt&Pepper and Gaussian white noise.

Conclusions:

  • The proposed algorithm provides accurate and robust retinal vessel network extraction.
  • It shows significant potential for clinical applications in automated retinal image analysis.
  • The method offers an improvement over existing techniques, particularly in challenging, noisy environments.