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

Overcoming five key challenges to make the energy transition a just labor transition.

Nature communications·2025
Same author

Monkey king evolution (MKE)-GA-SVM model for subtype classification of breast cancer.

Digital health·2024
Same author

Firefly-SVM predictive model for breast cancer subgroup classification with clinicopathological parameters.

Digital health·2023
Same author

An Unsupervised Fuzzy Clustering Approach for Early Screening of COVID-19 From Radiological Images.

IEEE transactions on fuzzy systems : a publication of the IEEE Neural Networks Council·2022
Same author

SUFEMO: A superpixel based fuzzy image segmentation method for COVID-19 radiological image elucidation.

Applied soft computing·2022
Same author

Breast Cancer Subtypes Classification with Hybrid Machine Learning Model.

Methods of information in medicine·2022

Related Experiment Video

Updated: Nov 24, 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

3.1K

Fuzzy Electromagnetism Optimization (FEMO) and its application in biomedical image segmentation.

Shouvik Chakraborty1, Kalyani Mali1

  • 1Department of Computer Science & Engineering, University of Kalyani, India.

Applied Soft Computing
|October 26, 2020
PubMed
Summary

A new Fuzzy Electromagnetism Optimization (FEMO) method enhances biomedical image segmentation. This unsupervised approach improves accuracy and efficiency without needing initial cluster center selection, outperforming existing methods.

Keywords:
Biomedical image segmentationElectromagnetism-like optimizationEvolutionary algorithmsFEMOFuzzy C-means clustering

More Related Videos

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

9.2K
Hybrid µCT-FMT imaging and image analysis
13:45

Hybrid µCT-FMT imaging and image analysis

Published on: June 4, 2015

13.4K

Related Experiment Videos

Last Updated: Nov 24, 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

3.1K
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

9.2K
Hybrid µCT-FMT imaging and image analysis
13:45

Hybrid µCT-FMT imaging and image analysis

Published on: June 4, 2015

13.4K

Area of Science:

  • Biomedical Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Biomedical image segmentation is crucial for medical diagnosis and analysis.
  • Existing unsupervised methods often struggle with accuracy and efficiency across different image modalities.
  • The Electromagnetism-like Optimization (EMO) method offers a promising basis for optimization but requires enhancement for complex segmentation tasks.

Purpose of the Study:

  • To introduce a novel unsupervised classification approach, Fuzzy Electromagnetism Optimization (FEMO), for biomedical image segmentation.
  • To enhance the efficiency and accuracy of biomedical image segmentation using a modified EMO and fuzzy C-Means algorithm.
  • To evaluate the performance of FEMO against established metaheuristic and evolutionary algorithms.

Main Methods:

  • The proposed Fuzzy Electromagnetism Optimization (FEMO) method combines a modified type 2 fuzzy C-Means algorithm with the Electromagnetism-like Optimization (EMO) algorithm.
  • FEMO utilizes fuzzy membership and EMO to determine optimal cluster center positions, eliminating dependency on initial selections.
  • The approach was validated using standard segmentation evaluation indices such as Davies-Bouldin, Xie-Beni, and Dunn index.

Main Results:

  • FEMO demonstrated superior performance in biomedical image segmentation compared to Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and EMO.
  • The proposed method achieved a lower Davies-Bouldin index (1.456889343 for 3 clusters) compared to other methods.
  • Qualitative and quantitative evaluations confirmed that FEMO outperforms existing methods on standard evaluation parameters.

Conclusions:

  • The Fuzzy Electromagnetism Optimization (FEMO) method is an effective and efficient unsupervised approach for biomedical image segmentation.
  • FEMO's ability to adapt to different image modalities and its independence from initial cluster center selection make it a valuable tool.
  • The study highlights FEMO's potential to advance the field of medical image analysis through improved segmentation accuracy.