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

Efficient brain tumor detection and classification using magnetic resonance imaging.

Biomedical physics & engineering express·2021
Same author

Wearable IoT Smart-Log Patch: An Edge Computing-Based Bayesian Deep Learning Network System for Multi Access Physical Monitoring System.

Sensors (Basel, Switzerland)·2019
Same author

Internet of Things with Maximal Overlap Discrete Wavelet Transform for Remote Health Monitoring of Abnormal ECG Signals.

Journal of medical systems·2018
See all related articles

Related Experiment Video

Updated: Aug 17, 2025

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

48.3K

FMTM-feature-map-based transform model for brain image segmentation in tumor detection.

Revathi Sundarasekar1, Ahilan Appathurai2

  • 1Research Scholar, PSN College of Engineering and Technology, Tirunelveli, India.

Network (Bristol, England)
|December 14, 2022
PubMed
Summary

This study introduces a Feature-Map based Transform Model (FMTM) for accurate brain tumour segmentation. The model effectively handles image heterogeneity, improving tumour detection precision and performance.

Keywords:
Feature mapimage segmentationtransform modelunsupervised learning

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
Patient-Specific Polyvinyl Alcohol Phantom Fabrication with Ultrasound and X-Ray Contrast for Brain Tumor Surgery Planning
08:41

Patient-Specific Polyvinyl Alcohol Phantom Fabrication with Ultrasound and X-Ray Contrast for Brain Tumor Surgery Planning

Published on: July 14, 2020

8.6K

Related Experiment Videos

Last Updated: Aug 17, 2025

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

48.3K
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
Patient-Specific Polyvinyl Alcohol Phantom Fabrication with Ultrasound and X-Ray Contrast for Brain Tumor Surgery Planning
08:41

Patient-Specific Polyvinyl Alcohol Phantom Fabrication with Ultrasound and X-Ray Contrast for Brain Tumor Surgery Planning

Published on: July 14, 2020

8.6K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neuroscience

Background:

  • Accurate brain image segmentation is vital for detecting physiological changes and analyzing brain functions.
  • Brain tumour segmentation presents significant challenges due to image heterogeneity, impacting detection accuracy.
  • Existing methods struggle with the complex variations in brain image features.

Purpose of the Study:

  • To introduce a novel Feature-Map based Transform Model (FMTM) for enhanced brain tumour segmentation.
  • To address the challenge of image heterogeneity in brain scans for improved tumour detection.
  • To develop a robust method for accurate and reliable identification of brain tumours.

Main Methods:

  • The Feature-Map based Transform Model (FMTM) utilizes transition Fourier mapping to analyze heterogeneous image features and intensity variations.
  • Unsupervised machine learning is employed for reliable characteristic map recognition within the mapping process.
  • The method incorporates symmetry and texture analysis for determining tumour severity and variability, with iterative learning to enhance precision.

Main Results:

  • The FMTM model demonstrated effective automatic feature extraction for brain tumour segmentation.
  • The proposed model achieved accurate and steady performance, leveraging the power of transition Fourier methods.
  • Performance was validated using metrics including processing time, precision, accuracy, and F1-Score.

Conclusions:

  • The Feature-Map based Transform Model (FMTM) offers a promising approach for accurate and robust brain tumour segmentation.
  • The model's ability to handle image heterogeneity leads to improved tumour detection outcomes.
  • FMTM provides a reliable method for quantitative analysis in neuroimaging studies.