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

Comparison study of population-based methods for non-invasive fetal electrocardiography extraction.

Frontiers in medicine·2026
Same author

Video-based hand gesture recognition via SPD manifold spatial representation and optical flow motion features.

PloS one·2026
Same author

Optimizing image watermarking integrity and visual quality via DTPSO and hybrid transform methods.

Scientific reports·2026
Same author

GFTrans: an on-the-fly static analysis framework for code performance profiling.

Frontiers in big data·2026
Same author

An enhanced neural network algorithm and its applications for numerical optimization and parameter extraction of photovoltaic models.

Scientific reports·2026
Same author

Structured dissociative PCA methods for high dimensional neuroimaging signal decomposition.

Scientific reports·2026

Related Experiment Video

Updated: Aug 18, 2025

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

2.9K

Harris hawks optimization for COVID-19 diagnosis based on multi-threshold image segmentation.

Mohammad Hashem Ryalat1, Osama Dorgham1,2, Sara Tedmori3

  • 1Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt, 19117 Jordan.

Neural Computing & Applications
|December 6, 2022
PubMed
Summary

This study introduces a faster method for medical image segmentation using Harris Hawks Optimization with Otsu's method. The new approach significantly reduces computational cost and time for analyzing chest images, including those from COVID-19 patients.

Keywords:
CT imagesCovid-19Harris hawks optimizationImage segmentationMultilevel thresholdingOtsu method

More Related Videos

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

14.3K
Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
07:05

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Published on: February 15, 2022

2.7K

Related Experiment Videos

Last Updated: Aug 18, 2025

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

2.9K
Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

14.3K
Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
07:05

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Published on: February 15, 2022

2.7K

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Artificial Intelligence

Background:

  • Digital image processing aids medical experts in disease diagnosis.
  • Image segmentation simplifies medical image analysis, with multilevel thresholding being highly effective.
  • Traditional methods like Otsu's suffer from high computational costs for multilevel thresholding.

Purpose of the Study:

  • To reduce computational cost in multilevel thresholding for medical image segmentation.
  • To maintain optimal segmentation outcomes while improving efficiency.
  • To apply and validate the proposed method on COVID-19 chest imaging data.

Main Methods:

  • Combining Harris Hawks Optimization with Otsu's method for image thresholding.
  • Implementing multilevel thresholding for enhanced image segmentation.
  • Testing the approach on publicly available chest imaging datasets, including COVID-19-AR population data.

Main Results:

  • Significant reduction in computational cost and convergence time compared to traditional Otsu's method.
  • Maintained high-quality segmentation results comparable to Otsu's method.
  • Demonstrated effectiveness on chest images from a rural COVID-19-positive population.

Conclusions:

  • The Harris Hawks Optimization combined with Otsu's method offers an efficient solution for medical image segmentation.
  • This approach provides a substantial improvement in computational efficiency without compromising segmentation accuracy.
  • The method is well-suited for analyzing medical images, particularly in the context of diseases like COVID-19.