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

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

5.3K
Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
5.3K

You might also read

Related Articles

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

Sort by
Same author

Nomogram for Predicting Lymph Node Involvement in Triple-Negative Breast Cancer.

Frontiers in oncology·2020
Same author

Extended transcriptome analysis reveals genome-wide lncRNA-mediated epigenetic dysregulation in colorectal cancer.

Computational and structural biotechnology journal·2020
Same author

The Impact of Social Support on Public Anxiety amidst the COVID-19 Pandemic in China.

International journal of environmental research and public health·2020
Same author

Spleen Stiffness Predicts Survival after Transjugular Intrahepatic Portosystemic Shunt in Cirrhotic Patients.

BioMed research international·2020
Same author

MoS<sub>2</sub>-on-AlN Enables High-Performance MoS<sub>2</sub> Field-Effect Transistors through Strain Engineering.

ACS applied materials & interfaces·2020
Same author

OSI-027 Alleviates Oxaliplatin Chemoresistance in Gastric Cancer Cells by Suppressing P-gp Induction.

Current molecular medicine·2020

Related Experiment Video

Updated: Aug 1, 2025

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

9.0K

Energy minimization segmentation model based on MRI images.

Xiuxin Wang1,2, Yuling Yang1, Ting Wu1

  • 1Chongqing University of Posts and Telecommunications, Chongqing, China.

Frontiers in Neuroscience
|May 1, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an improved fuzzy clustering algorithm for Magnetic Resonance Imaging (MRI) segmentation. The enhanced method accurately segments brain tissues and lesions, overcoming challenges like partial volume effects.

Keywords:
MRIanatomical atlasenergyimage segmentationlesions filling

More Related Videos

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.7K
Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
09:30

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Published on: December 18, 2016

19.6K

Related Experiment Videos

Last Updated: Aug 1, 2025

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

9.0K
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.7K
Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
09:30

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Published on: December 18, 2016

19.6K

Area of Science:

  • Medical Imaging
  • Neuroscience
  • Computer Vision

Background:

  • Manual medical image segmentation is time-consuming and subjective.
  • Accurate segmentation of brain tissues and lesions is crucial for diagnosing brain diseases.
  • Existing automatic methods face challenges due to partial volume effects and lesions in MRI.

Purpose of the Study:

  • To develop an automatic and reliable method for medical image segmentation.
  • To address the impact of partial volume effect and multiple sclerosis lesions on MRI segmentation accuracy.
  • To improve the accuracy of brain tissue and lesion segmentation in MRI.

Main Methods:

  • Developed an energy-minimized segmentation algorithm based on fuzzy clustering.
  • Researched an improved objective function and a post-processing lesion filling method.
  • Proposed a multi-channel input energy-minimization segmentation method with lesion filling and anatomical mapping.

Main Results:

  • The proposed AR-FCM algorithm improves segmentation accuracy compared to RFCM, especially for boundary voxels.
  • The multi-channel segmentation method with lesion filling enhances overall segmentation accuracy.
  • Experimental verification confirmed the feasibility of the lesion filling strategy.

Conclusions:

  • The developed energy-minimized segmentation approach effectively improves MRI segmentation accuracy.
  • The lesion filling strategy is a feasible post-processing technique to increase segmentation precision.
  • This work contributes to more reliable and automated analysis of brain MRI scans.