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

An effective method for segmentation of MR brain images using the ant colony optimization algorithm.

Journal of digital imaging·2013
See all related articles

Related Experiment Video

Updated: Apr 26, 2026

A MRI-Based Toolbox for Neurosurgical Planning in Nonhuman Primates
08:41

A MRI-Based Toolbox for Neurosurgical Planning in Nonhuman Primates

Published on: July 17, 2020

4.4K

Skull removal in MR images using a modified artificial bee colony optimization algorithm.

Mohammad Taherdangkoo1

  • 1Department of Artificial Intelligence, Tehran Business School, Tehran, Iran.

Technology and Health Care : Official Journal of the European Society for Engineering and Medicine
|July 26, 2014
PubMed
Summary

This study introduces a novel Artificial Bee Colony (ABC) algorithm for efficient skull removal in brain Magnetic Resonance (MR) images. The enhanced algorithm offers superior performance for accurate brain image analysis and potential clinical applications.

Keywords:
MRI segmentationSkull bone regionant colony optimization (ACO)artificial bee colony (ABC)particle swarm optimization (PSO)

More Related Videos

Tactile Conditioning And Movement Analysis Of Antennal Sampling Strategies In Honey Bees Apis mellifera L.
10:14

Tactile Conditioning And Movement Analysis Of Antennal Sampling Strategies In Honey Bees Apis mellifera L.

Published on: December 12, 2012

10.2K
Simultaneous Long-term Recordings at Two Neuronal Processing Stages in Behaving Honeybees
13:55

Simultaneous Long-term Recordings at Two Neuronal Processing Stages in Behaving Honeybees

Published on: July 21, 2014

14.3K

Related Experiment Videos

Last Updated: Apr 26, 2026

A MRI-Based Toolbox for Neurosurgical Planning in Nonhuman Primates
08:41

A MRI-Based Toolbox for Neurosurgical Planning in Nonhuman Primates

Published on: July 17, 2020

4.4K
Tactile Conditioning And Movement Analysis Of Antennal Sampling Strategies In Honey Bees Apis mellifera L.
10:14

Tactile Conditioning And Movement Analysis Of Antennal Sampling Strategies In Honey Bees Apis mellifera L.

Published on: December 12, 2012

10.2K
Simultaneous Long-term Recordings at Two Neuronal Processing Stages in Behaving Honeybees
13:55

Simultaneous Long-term Recordings at Two Neuronal Processing Stages in Behaving Honeybees

Published on: July 21, 2014

14.3K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • Skull removal is a critical preprocessing step for brain Magnetic Resonance (MR) image analysis.
  • Accurate segmentation and analysis of brain structures depend on effective removal of non-brain tissues.
  • Existing skull removal methods may have limitations in accuracy or computational efficiency.

Purpose of the Study:

  • To develop and evaluate a novel algorithm for skull stripping in brain MR images.
  • To improve the accuracy and efficiency of skull removal using an optimized Artificial Bee Colony (ABC) algorithm.
  • To compare the proposed algorithm's performance against established optimization techniques.

Main Methods:

  • A modified Artificial Bee Colony (ABC) optimization algorithm was developed for skull removal.
  • Novel strategies for initializing scout bees and their search direction were implemented.
  • An additional constraint was introduced to prevent discontinuous regions in the processed images.
  • The algorithm was tested on de-identified MR brain images from various scanner models.

Main Results:

  • The proposed algorithm successfully removed the bony skull from all tested MR brain images.
  • Comparative analysis showed superior results and computational performance against Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO).
  • The modified ABC algorithm demonstrated effectiveness across different MR scanner types.

Conclusions:

  • The developed ABC-based algorithm provides an effective and efficient solution for skull stripping in brain MR images.
  • The algorithm's performance suggests significant potential for integration into clinical neuroimaging workflows.
  • This method offers an advancement in automated brain image preprocessing for enhanced analysis.