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 Experiment Video

Updated: May 22, 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

Automatic white matter lesion segmentation using an adaptive outlier detection method.

Kok Haur Ong1, Dhanesh Ramachandram, Rajeswari Mandava

  • 1Computer Vision Research Group, School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang, Malaysia. kokhaur@cs.usm.my

Magnetic Resonance Imaging
|May 15, 2012
PubMed
Summary
This summary is machine-generated.

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

A Retrospective Analysis of Deep Tissue Pressure Injury Prevalence and Incidence Using a Large-scale Wound Care Database in Long-term Care Settings Across North America.

Advances in skin & wound care·2026
Same author

Development, validation, and pilot implementation of a prognostic model to predict future foot complications among people with diabetes recently discharged from hospital.

Journal of clinical epidemiology·2026
Same author

Three‑dimensional immune cartography uncovers subclinical remodeling in psoriasis.

Scientific reports·2026
Same author

Cytological Classification Diagnosis for Thyroid Nodules via Multimodal Model Deep Learning.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025
Same author

A Segmentation-based Automated Calculation of Patient Size and Size-specific Dose Estimates in Pediatric Computed Tomography Scans.

Journal of medical physics·2024
Same author

A comprehensive AI model development framework for consistent Gleason grading.

Communications medicine·2024
Same journal

Systematic comparison of MPRAGE and BRAVO T1-weighted MRI pulse sequences and brain morphometry in high-risk young adults.

Magnetic resonance imaging·2026
Same journal

Foot dynamic contrast-enhanced MRI for assessing microcirculatory changes after endovascular therapy in peripheral artery disease: A prospective pilot study.

Magnetic resonance imaging·2026
Same journal

Reconstruction of MRI from undersampled k-spaces of double-contrast volume acquisitions using deep neural networks.

Magnetic resonance imaging·2026
Same journal

Radiofrequency-induced heating safety of brain MRI scans at 7 T in the presence of a shoulder implant.

Magnetic resonance imaging·2026
Same journal

Incremental diagnostic value of microstructural time-dependent diffusion MRI in differentiating PCNSL from glioblastoma over conventional MRI.

Magnetic resonance imaging·2026
Same journal

Enhanced respiratory motion compensation in free-breathing dynamic contrast-enhanced MRI with GROG-facilitated bunch phase encoding and Golden angle radial sampling.

Magnetic resonance imaging·2026
See all related articles

This study introduces an automated method for segmenting white matter (WM) lesions in MRI scans. The novel approach accurately detects WM lesions, correlating highly with expert manual segmentation.

Area of Science:

  • Neuroimaging
  • Medical image analysis
  • Computational neuroscience

Background:

  • White matter (WM) lesions are abnormalities visible in MRI scans.
  • These lesions are indicators of neurological conditions like stroke, multiple sclerosis, and dementia.
  • Accurate segmentation of WM lesions is crucial for diagnosis and monitoring.

Purpose of the Study:

  • To present a novel automated method for segmenting white matter lesions in MRI.
  • To improve the accuracy and efficiency of WM lesion detection and quantification.
  • To validate the proposed method against expert manual segmentation and existing algorithms.

Main Methods:

  • Developed an automated method using adaptive outlier detection on FLAIR MRI sequences.
  • Employed a novel adaptive trimmed mean algorithm and box-whisker plot for outlier identification.

More Related Videos

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

Manual Segmentation of the Human Choroid Plexus Using Brain MRI
04:25

Manual Segmentation of the Human Choroid Plexus Using Brain MRI

Published on: December 15, 2023

Related Experiment Videos

Last Updated: May 22, 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

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

Manual Segmentation of the Human Choroid Plexus Using Brain MRI
04:25

Manual Segmentation of the Human Choroid Plexus Using Brain MRI

Published on: December 15, 2023

  • Implemented pre- and postprocessing steps to mitigate MRI artifacts and reduce false positives.

    • Main Results:

    • Achieved a significant correlation (R=0.9641) between automated and manual WM lesion segmentation.
    • Demonstrated high accuracy in lesion volume comparison with expert radiologist segmentation.
    • The proposed approach showed competitive performance against leading lesion segmentation algorithms on a benchmark dataset.

    Conclusions:

    • The proposed automated method provides accurate and reliable segmentation of white matter lesions.
    • This technique has the potential to aid in the diagnosis and management of various brain disorders.
    • The adaptive outlier detection approach offers a robust solution for WM lesion analysis in clinical neuroimaging.