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

Imidazopyridine derivatives as promising anti-<i>Toxoplasma gondii</i> agents: <i>in vitro</i> studies, <i>in vivo</i> testing, and molecular modeling analysis.

RSC advances·2026
Same author

Impact of a clinical engagement targeted antimicrobial stewardship program on antimicrobial use in Pakistan: a multicenter longitudinal point prevalence survey.

Expert review of anti-infective therapy·2026
Same author

The consequences of <i>Shigella</i> medically-attended diarrhoea and other leading pathogens among young children living in high-burden settings: a multi-country prospective cohort study.

EClinicalMedicine·2026
Same author

Frequency and correlates of non-receipt of age-appropriate vaccination among children aged 6-35 months with medically attended diarrhea: Findings from the Enterics for Global Health (EFGH) Shigella study, 2022-2024.

PLOS global public health·2026
Same author

Robot-Assisted Coronary Artery Bypass Versus Percutaneous Coronary Intervention in Patients With Coronary Artery Disease: A Meta-Analysis.

Innovations (Philadelphia, Pa.)·2026
Same author

Pullback pressure gradients: Redefining the functional landscape of coronary artery disease.

Cardiovascular revascularization medicine : including molecular interventions·2026

Related Experiment Video

Updated: Jun 16, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.5K

Medical image segmentation approach based on hybrid adaptive differential evolution and crayfish optimizer.

Reham R Mostafa1, Ahmed M Khedr2, Zaher Al Aghbari2

  • 1Big Data Mining and Multimedia Research Group, Centre for Data Analytics and Cybersecurity (CDAC), Research Institute of Sciences and Engineering (RISE), University of Sharjah, Sharjah 27272, United Arab Emirates; Information Systems Department, Faculty of Computers and Information Sciences, Mansoura University, Mansoura 35516, Egypt.

Computers in Biology and Medicine
|August 15, 2024
PubMed
Summary

A novel hybrid optimization algorithm, HADECO, enhances multi-threshold image segmentation for medical imaging. This method improves diagnostic accuracy by efficiently segmenting tumors and lesions in MRI and CT scans.

Keywords:
Crayfish optimization algorithmDifferential evolutionImage segmentationQuadratic interpolation strategyWavelet function

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

2.7K
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

8.6K

Related Experiment Videos

Last Updated: Jun 16, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.5K
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

2.7K
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

8.6K

Area of Science:

  • Medical Image Analysis
  • Computational Intelligence
  • Optimization Algorithms

Background:

  • Medical image segmentation is crucial for diagnostics but challenged by multilevel thresholding's complexity.
  • Traditional methods struggle with NP-hard optimization problems in threshold determination.
  • Efficient strategies are needed to improve segmentation accuracy and analysis.

Purpose of the Study:

  • To introduce an efficient multi-threshold image segmentation (MTIS) method using a hybrid optimization algorithm.
  • To enhance the accuracy and efficiency of medical image analysis and diagnosis.
  • To address the computational complexity of multilevel thresholding.

Main Methods:

  • Developed HADECO, a hybrid algorithm combining Differential Evolution (DE) and Crayfish Optimization Algorithm (COA) with information exchange.
  • Employed Latin Hypercube Sampling (LHS) for initial population generation.
  • Introduced an improved DE (IDE) with adaptive parameters and an adaptive COA (ACOA) for balanced exploration and exploitation.

Main Results:

  • HADECO demonstrated superior optimization capabilities, achieving the lowest average Friedman rank (1.08) against contemporary algorithms.
  • The HADECO-based MTIS method showed improved quantitative results in segmenting brain intracranial hemorrhage (ICH) and knee MRI images.
  • Achieved superior average Peak Signal-to-Noise Ratio (PSNR) and Feature Similarity Index (FSIM) in both brain (1.5, 1.7) and knee (1.3, 1.2) image segmentation tasks.

Conclusions:

  • The proposed HADECO algorithm effectively addresses the challenges of multi-threshold image segmentation.
  • The HADECO-based MTIS method significantly enhances segmentation accuracy and efficiency in medical imaging applications.
  • This approach offers a promising solution for precise tumor and lesion isolation, improving diagnostic capabilities.