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

Trial and Error and Algorithm01:12

Trial and Error and Algorithm

424
A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
424
Brain Imaging01:14

Brain Imaging

747
Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
747
Optimal Foraging00:48

Optimal Foraging

13.9K
How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.
13.9K
Optimization Problems01:26

Optimization Problems

77
Optimization problems often involve identifying maximum or minimum values under specific constraints. A well-known example is determining the longest horizontal pipe that can be moved around a right-angled corner, where a 3-meter-wide hallway meets a 2-meter-wide hallway. This scenario, common in architectural design and industrial transport, can be understood conceptually through geometric and trigonometric reasoning.To visualize the problem, consider the pipe as a straight line that touches...
77
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

319
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
319
Optimal Arousal Theory01:23

Optimal Arousal Theory

872
The optimal arousal theory suggests that performance is maximized when an individual experiences a moderate level of arousal. This theory is closely tied to the Yerkes-Dodson law, which illustrates an inverted U-shaped relationship between arousal and performance. The law, formulated by psychologists Robert Yerkes and John Dodson, implies an ideal arousal level for optimal performance, and deviations from this level can lead to declines in effectiveness.
Inverted U-Shaped Performance Curve
The...
872

You might also read

Related Articles

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

Sort by
Same author

Prediction of Clinically Significant Depressive Symptoms at 2-Year Follow-Up in Older Adults: Machine Learning Study Using the English Longitudinal Study of Ageing.

JMIR formative research·2026
Same author

Early sepsis prediction using a hybrid LSTM-GAT model: a study on the PhysioNet 2019 dataset.

BMJ health & care informatics·2026
Same author

SPECT-MPI iterative denoising during the reconstruction process using a two-phase learned convolutional neural network.

EJNMMI physics·2024
Same author

A Review on Automated Sleep Study.

Annals of biomedical engineering·2024
Same author

Performance Improvement in Brain Tumor Detection in MRI Images Using a Combination of Evolutionary Algorithms and Active Contour Method.

Journal of digital imaging·2021
Same author

Enhanced Ultra-Sensitive Metamaterial Resonance Sensor based on Double Corrugated Metal stripe for Terahertz Sensing.

Scientific reports·2019
Same journal

Bayesian Convolutional Neural Networks in Medical Imaging Classification: A Promising Solution for Deep Learning Limits in Data Scarcity Scenarios.

Journal of digital imaging·2023
Same journal

Detecting and Characterizing Inferior Vena Cava Filters on Abdominal Computed Tomography with Data-Driven Computational Frameworks.

Journal of digital imaging·2023
Same journal

DMCA-GAN: Dual Multilevel Constrained Attention GAN for MRI-Based Hippocampus Segmentation.

Journal of digital imaging·2023
Same journal

Left Ventricular Myocardial Dysfunction Evaluation in Thalassemia Patients Using Echocardiographic Radiomic Features and Machine Learning Algorithms.

Journal of digital imaging·2023
Same journal

Public Imaging Datasets of Gastrointestinal Endoscopy for Artificial Intelligence: a Review.

Journal of digital imaging·2023
Same journal

External Validation of Robust Radiomic Signature to Predict 2-Year Overall Survival in Non-Small-Cell Lung Cancer.

Journal of digital imaging·2023
See all related articles

Related Experiment Video

Updated: Feb 6, 2026

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

3.8K

A New Optimized Thresholding Method Using Ant Colony Algorithm for MR Brain Image Segmentation.

Bahar Khorram1, Mehran Yazdi2

  • 1School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran.

Journal of Digital Imaging
|August 10, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces an optimized ant colony algorithm for segmenting magnetic resonance (MR) brain images. The novel method accurately identifies brain tissues, aiding in the diagnosis of neurological disorders.

Keywords:
Ant colony optimizationMR brain imagesMeta-heuristic algorithmsMultilevel thresholdingSegmentationTextural feature

More Related Videos

Collection and Long-Term Maintenance of Leaf-Cutting Ants Atta in Laboratory Conditions
10:11

Collection and Long-Term Maintenance of Leaf-Cutting Ants Atta in Laboratory Conditions

Published on: August 30, 2022

4.2K
Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

49.4K

Related Experiment Videos

Last Updated: Feb 6, 2026

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

3.8K
Collection and Long-Term Maintenance of Leaf-Cutting Ants Atta in Laboratory Conditions
10:11

Collection and Long-Term Maintenance of Leaf-Cutting Ants Atta in Laboratory Conditions

Published on: August 30, 2022

4.2K
Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

49.4K

Area of Science:

  • Medical Image Processing
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Accurate brain image segmentation is crucial for diagnosing neurological disorders by analyzing brain volume changes.
  • Manual segmentation of MR brain images is time-consuming and challenging, necessitating automated methods.
  • Quantifying brain tissue volumes (white matter, gray matter, cerebrospinal fluid) aids in disease assessment, such as Alzheimer's and epilepsy.

Purpose of the Study:

  • To propose an optimized thresholding method for MR brain image segmentation.
  • To leverage a biologically inspired ant colony algorithm for enhanced segmentation accuracy.
  • To improve segmentation performance through post-processing image enhancement.

Main Methods:

  • Developed an optimized thresholding technique using an ant colony algorithm.
  • Incorporated textural features as heuristic information within the ant colony algorithm.
  • Applied post-processing image enhancement based on homogeneity for improved results.

Main Results:

  • The proposed method demonstrated competitive accuracy compared to traditional meta-heuristic methods, K-means, and expectation maximization.
  • Empirical results on axial T1-weighted MR brain images validated the algorithm's effectiveness.
  • The ant colony algorithm achieved reliable segmentation of brain tissues.

Conclusions:

  • The optimized ant colony algorithm offers an effective and accurate approach for MR brain image segmentation.
  • This method provides a valuable tool for automated diagnosis and surgical planning in neuroimaging.
  • The integration of textural features and homogeneity-based enhancement significantly boosts segmentation performance.