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

Computed Tomography01:10

Computed Tomography

7.6K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
7.6K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Multiple-Strategies dung beetle optimizer and its applications in engineering optimization and bankruptcy prediction.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Bisphenol A promotes esophageal carcinogenesis by activating the MMP1-PCOLCE regulatory axis and remodeling the tumor immune microenvironment.

Naunyn-Schmiedeberg's archives of pharmacology·2026
Same author

The translational roles of circular RNAs in cancers and their underlying molecular mechanisms.

Medical oncology (Northwood, London, England)·2026
Same author

Venetoclax plus Idarubicin and cytarabine as frontline induction for newly diagnosed acute myeloid leukemia in young, fit adults: a real-world study.

Annals of hematology·2026
Same author

Clinicopathological characteristics of alveolar adenoma.

Frontiers in oncology·2026
Same author

Erratum to "High-throughput screening of ancient forest plant extracts shows cytotoxicity towards triple-negative breast cancer" [Environ. Int. 181 (2023) 108279].

Environment international·2026
Same journal

Correction: A method for supervoxel-wise association studies of age and other non-imaging variables from coronary computed tomography angiograms.

Scientific reports·2026
Same journal

Poly(bromophenol blue)/CoSn(OH)<sub>6</sub> cubic particles modified pencil graphite electrode for electrochemical determination of diphenhydramine.

Scientific reports·2026
Same journal

Dietary Chlorella, Spirulina, and acidifier modulate jejunal cytokine-related gene expression in broiler chickens.

Scientific reports·2026
Same journal

Perceived physical activity barriers in university students: associations with fatigue and eating behaviours.

Scientific reports·2026
Same journal

Refuge limitation structures habitat use in agricultural landscapes: evidence from Sunda pangolins.

Scientific reports·2026
Same journal

Lightweight stateless transaction verification with outsourced witness updates for UTXO blockchains.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: May 5, 2026

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

9.4K

Gaussian bare‑bone JAYA algorithm for multi-threshold medical image segmentation.

Shengbo Yang1, Guodao Zhang2,3, Bolun Zheng4

  • 1Division of Pulmonary Medicine, Wenzhou Central Hospital, Wenzhou, 325000, Zhejiang, China.

Scientific Reports
|November 29, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces GBJAYA, an enhanced optimization algorithm for medical image segmentation. It achieves superior performance and stability, overcoming limitations of traditional methods for better disease detection.

Keywords:
2D kapur’s entropyJAYA optimization algorithmMedical image segmentationMulti-threshold image segmentationSwarm intelligence optimization

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

3.3K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

725

Related Experiment Videos

Last Updated: May 5, 2026

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

9.4K
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

3.3K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

725

Area of Science:

  • Medical Imaging
  • Computational Intelligence
  • Algorithm Optimization

Background:

  • Medical image segmentation is vital for diagnosis and treatment planning.
  • Traditional methods face challenges like high computational cost and local optima.
  • There's a need for improved segmentation algorithms.

Purpose of the Study:

  • To introduce GBJAYA, an enhanced optimization algorithm for medical image segmentation.
  • To improve global search capabilities and convergence speed.
  • To address limitations of existing multi-threshold segmentation techniques.

Main Methods:

  • Developed the GBJAYA algorithm, integrating a Gaussian bare-bone strategy.
  • Incorporated Gaussian-distributed random number updates for enhanced global search.
  • Evaluated performance on IEEE CEC2017 benchmark functions and medical images.

Main Results:

  • GBJAYA outperformed 20 other algorithms in benchmark and medical image tests.
  • Achieved lower mean values and standard deviations, indicating superior performance.
  • Demonstrated rapid convergence and avoidance of local optima.

Conclusions:

  • GBJAYA significantly enhances medical image segmentation.
  • The algorithm offers superior performance, stability, and fast convergence.
  • GBJAYA shows broad potential for medical diagnosis and treatment planning.