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: May 8, 2026

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

Multiple active contours guided by differential evolution for medical image segmentation.

I Cruz-Aceves1, J G Avina-Cervantes, J M Lopez-Hernandez

  • 1Universidad de Guanajuato, Division de Ingenierias Campus, Irapuato-Salamanca, Carretera Salamanca-Valle de Santiago, Salamanca, GTO, Mexico.i.cruzaceves@ugto.mx

Computational and Mathematical Methods in Medicine
|August 29, 2013
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

Numerical study of complex dynamics and extreme events within noise-like pulses from an erbium figure-eight laser.

Optics express·2019
Same author

Suspended LRSPP for the development of highly integrated active plasmonic devices.

Optics express·2019
Same author

Evolutionary Spiking Neural Networks for Solving Supervised Classification Problems.

Computational intelligence and neuroscience·2019
Same author

Hadamard circular masks: high focal depth with high throughput.

Optics express·2017
Same author

Quadrupedal Robot Locomotion: A Biologically Inspired Approach and Its Hardware Implementation.

Computational intelligence and neuroscience·2016
Same author

Supercontinuum optimization for dual-soliton based light sources using genetic algorithms in a grid platform.

Optics express·2014
Same journal

Correction to "Mathematical Modelling of COVID-19 Transmission in Kenya: A Model with Reinfection Transmission Mechanism".

Computational and mathematical methods in medicine·2025
Same journal

RETRACTION: Ligustrazine Inhibits Lung Phosphodiesterase Activity in a Rat Model of Allergic Asthma.

Computational and mathematical methods in medicine·2025
Same journal

RETRACTION: Delivery of miR-224-5p by Exosomes from Cancer-Associated Fibroblasts Potentiates Progression of Clear Cell Renal Cell Carcinoma.

Computational and mathematical methods in medicine·2025
Same journal

RETRACTION: Empirical Analysis of the Nursing Effect of Intelligent Medical Internet of Things in Postoperative Osteoarthritis.

Computational and mathematical methods in medicine·2025
Same journal

RETRACTION: Evaluation and Analysis of the Intervention Effect of Systematic Parent Training Based on Computational Intelligence on Child Autism.

Computational and mathematical methods in medicine·2024
Same journal

RETRACTION: Humanistic Spirit Training of Medical Students Based on Multisource Medical Data Fusion.

Computational and mathematical methods in medicine·2024
See all related articles

A novel image segmentation method, MACDE, uses differential evolution for enhanced active contour capabilities. This robust method accurately segments medical images like CT and MRI scans, outperforming existing techniques.

Area of Science:

  • Medical image analysis
  • Computer vision
  • Computational intelligence

Background:

  • Image segmentation is crucial for medical diagnosis.
  • Classical active contour models have limitations in complex scenarios.
  • Differential evolution offers robust optimization capabilities.

Purpose of the Study:

  • To introduce a new image segmentation method, MACDE, enhancing active contour models.
  • To improve exploration and exploitation in segmentation using differential evolution.
  • To evaluate MACDE's performance on synthetic and real medical image datasets.

Main Methods:

  • Developed MACDE using multiple active contours guided by differential evolution.
  • Employed a polar coordinate system to enhance differential evolution.

More Related Videos

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

Related Experiment Videos

Last Updated: May 8, 2026

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

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

  • Validated MACDE on synthetic images with noise and concavities.
  • Applied MACDE to segment human heart and left ventricle in CT and MRI datasets.

    • Main Results:

    • MACDE demonstrated superior efficiency and robustness compared to classical active contour and Tseng methods.
    • Achieved high accuracy in segmenting complex synthetic images.
    • Successfully segmented human heart and left ventricle in medical imaging datasets.
    • Quantitative and qualitative evaluations confirmed MACDE's effectiveness.

    Conclusions:

    • MACDE offers a significant advancement in image segmentation, particularly for medical applications.
    • The method provides accurate and robust segmentation, outperforming existing approaches.
    • MACDE's differential evolution strategy enhances control point optimization.