Jove
Visualize
Contact Us

Related Experiment Video

Updated: Jun 20, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

Learning-based vertebra detection and iterative normalized-cut segmentation for spinal MRI.

Szu-Hao Huang1, Yi-Hong Chu, Shang-Hong Lai

  • 1Department of Computer Science, National Tsing Hua University, Hsinchu 300, Taiwan.

IEEE Transactions on Medical Imaging
|September 29, 2009
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

Relationship between sonography of sternocleidomastoid muscle and cervical passive range of motion in infants with congenital muscular torticollis.

Biomedical journal·2019
Same author

Learning component-level sparse representation for image and video categorization.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2013
Same author

Automatic distortion correction of endoscopic images captured with wide-angle zoom lens.

IEEE transactions on bio-medical engineering·2013
Same author

Reconstructing 3D Face Model with Associated Expression Deformation from a Single Face Image via Constructing a Low-Dimensional Expression Deformation Manifold.

IEEE transactions on pattern analysis and machine intelligence·2011
Same author

An orientation inference framework for surface reconstruction from unorganized point clouds.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2010
Same author

Compressibility-aware media retargeting with structure preserving.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2010
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
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

This study presents an automated system for vertebra detection and segmentation in spinal MR images. The system achieves high accuracy, improving intelligent diagnosis systems.

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Artificial Intelligence

Background:

  • Accurate vertebra localization is crucial for intelligent spinal magnetic resonance (MR) image analysis.
  • Existing methods often require manual intervention or lack sufficient accuracy.

Purpose of the Study:

  • To develop a fully automatic system for vertebra detection and segmentation in spinal MR images.
  • To enhance the accuracy and robustness of automated spinal image analysis.

Main Methods:

  • A three-stage approach: AdaBoost-based vertebra detection, curve fitting for refinement, and iterative normalized cut for segmentation.
  • Utilized an improved AdaBoost algorithm for efficient detection and robust estimation for refinement.
  • Employed an iterative normalized-cut algorithm for precise region segmentation.

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

Related Experiment Videos

Last Updated: Jun 20, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

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

Main Results:

  • Achieved nearly 98% vertebra detection rate and 96% segmentation accuracy.
  • Demonstrated superior performance compared to previous representative methods.
  • The system proved robust and accurate across diverse spinal MR images.

Conclusions:

  • The developed system offers a robust and accurate solution for automatic vertebra detection and segmentation.
  • It provides a valuable tool for advanced research and clinical applications in spinal MR image analysis.