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 Videos

An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation.

Alan Wee-Chung Liew1, Hong Yan

  • 1Department of Computer Engineering and Information Technology, City University of Hong Kong, Hong Kong. itwcliew@cityu.edu.hk

IEEE Transactions on Medical Imaging
|September 6, 2003
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

3D TractFormer: 3D Direct Volumetric White Matter Tract Segmentation with Hybrid Channel-Wise Transformer.

Sensors (Basel, Switzerland)·2026
Same author

A Hybrid Bidirectional Deep Learning Model Using HRV for Prediction of ICU Mortality Risk in TBI Patients.

Journal of healthcare informatics research·2025
Same author

Explainable multimodal hematology analysis for white blood cell classification and attribute prediction.

Computers in biology and medicine·2025
Same author

Identifying key physiological and clinical factors for traumatic brain injury patient management using network analysis and machine learning.

PloS one·2025
Same author

Galformer: a transformer with generative decoding and a hybrid loss function for multi-step stock market index prediction.

Scientific reports·2024
Same author

Integration of Multikinds Imputation With Covariance Adaptation Based on Evidence Theory.

IEEE transactions on neural networks and learning systems·2024
Same journal

LLM-enhanced Neuron Segmentation and Reconstruction in Complex Mouse Brain Images.

IEEE transactions on medical imaging·2026
Same journal

Matrixed-Spectrum Decomposition Accelerated Linear Boltzmann Transport Equation Solver for Fast Scatter Correction in Multi-Spectral CT.

IEEE transactions on medical imaging·2026
Same journal

The Ritz Adjoint Method for MRI Pulse Design.

IEEE transactions on medical imaging·2026
Same journal

Physiology-guided Self-supervised Learning for Simultaneous Dual-Tracer PET Separation.

IEEE transactions on medical imaging·2026
Same journal

Informed-Exploration Reinforcement Learning for Automated Virtual Coronary Intervention Planning.

IEEE transactions on medical imaging·2026
Same journal

4D Reconstruction of Fetal Left Ventricle from Echocardiography via 2.5D Radial Segmentation and Graph-Fourier Reconstruction.

IEEE transactions on medical imaging·2026
See all related articles

This study introduces an adaptive spatial fuzzy c-means algorithm for segmenting 3-D magnetic resonance (MR) images, effectively reducing noise and bias field artifacts for improved accuracy.

Area of Science:

  • Medical Imaging
  • Image Processing
  • Computational Biology

Background:

  • Three-dimensional (3-D) magnetic resonance (MR) imaging is crucial for medical diagnosis.
  • Segmentation of 3-D MR images is challenging due to noise and intensity nonuniformity (INU) artifacts.
  • Existing algorithms often struggle with noise reduction and accurate artifact correction.

Purpose of the Study:

  • To develop an adaptive spatial fuzzy c-means clustering algorithm for robust 3-D MR image segmentation.
  • To address the limitations of noise and INU artifacts in MR image analysis.
  • To improve the accuracy and reliability of MR image segmentation.

Main Methods:

  • An adaptive spatial fuzzy c-means algorithm incorporating spatial continuity constraints.

Related Experiment Videos

  • A dissimilarity index to model spatial interactions between image voxels.
  • Modeling the INU artifact as a multiplicative bias field using B-spline surfaces for efficient computation.

    • Main Results:

    • The proposed algorithm effectively reduces noise and classification ambiguity in 3-D MR images.
    • Accurate correction of intensity nonuniformity (INU) artifacts was achieved.
    • Demonstrated efficacy through extensive segmentation experiments on simulated and real MR images.

    Conclusions:

    • The adaptive spatial fuzzy c-means algorithm provides a robust solution for 3-D MR image segmentation.
    • The method significantly improves segmentation accuracy in the presence of noise and INU artifacts.
    • The algorithm offers a computationally efficient and effective approach for MR image analysis.