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

Classification of Bones01:18

Classification of Bones

14.3K
The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The...
14.3K

You might also read

Related Articles

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

Sort by
Same author

Joint Discriminative and Representative Feature Selection for Alzheimer's Disease Diagnosis.

Machine learning in medical imaging. MLMI (Workshop)·2017
Same author

Single- and Multiple-Shell Uniform Sampling Schemes for Diffusion MRI Using Spherical Codes.

IEEE transactions on medical imaging·2017
Same author

Functional Connectivity Network Fusion with Dynamic Thresholding for MCI Diagnosis.

Machine learning in medical imaging. MLMI (Workshop)·2017
Same author

Landmark-Based Alzheimer's Disease Diagnosis Using Longitudinal Structural MR Images.

Medical computer vision and Bayesian and graphical models for biomedical imaging : MICCAI 2016 international workshop, MCV and BAMBI, Athens, Greece, October 21, 2016 : revised selected papers·2017
Same author

Ensemble Hierarchical High-Order Functional Connectivity Networks for MCI Classification.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2017
Same author

Remodeling Pearson's Correlation for Functional Brain Network Estimation and Autism Spectrum Disorder Identification.

Frontiers in neuroinformatics·2017
Same journal

Correction to "On the shape of the radiation survival curve in tumor spheroids: The role of oxygen heterogeneity".

Medical physics·2026
Same journal

Multi-view constrained semi-supervised vertebra detection for 3D ultrasound spine volume.

Medical physics·2026
Same journal

Accuracy of quantitative <sup>177</sup>Lu SPECT/CT imaging: A systematic review.

Medical physics·2026
Same journal

Physics-constrained dual-domain network for CBCT reconstruction from orthogonal X-rays in gynecologic radiotherapy.

Medical physics·2026
Same journal

Decomposition-based harmonization for quantitative PET imaging across scanners and radiotracers.

Medical physics·2026
Same journal

Development and evaluation of an in vivo dose-based monitoring system for electron FLASH radiation therapy.

Medical physics·2026
See all related articles

Related Experiment Video

Updated: May 1, 2026

Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin
09:36

Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin

Published on: March 14, 2018

8.9K

Automated bone segmentation from dental CBCT images using patch-based sparse representation and convex optimization.

Li Wang1, Ken Chung Chen2, Yaozong Gao1

  • 1Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599.

Medical Physics
|April 4, 2014
PubMed
Summary
This summary is machine-generated.

A new method for segmenting cone-beam computed tomography (CBCT) images uses patch-based sparse representation for improved accuracy in diagnosing craniomaxillofacial deformities. This technique enhances 3D model generation for better patient treatment planning.

More Related Videos

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

335
Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
09:10

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

2.0K

Related Experiment Videos

Last Updated: May 1, 2026

Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin
09:36

Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin

Published on: March 14, 2018

8.9K
Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

335
Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
09:10

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

2.0K

Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Biomedical Engineering

Background:

  • Cone-beam computed tomography (CBCT) is vital for craniomaxillofacial (CMF) deformity diagnosis and treatment planning.
  • Accurate segmentation of CBCT images is crucial for generating 3D models.
  • Poor image quality and artifacts in CBCT present significant segmentation challenges.

Purpose of the Study:

  • To develop an automatic segmentation method for CBCT images.
  • To address challenges posed by low signal-to-noise ratio and image artifacts.
  • To improve the accuracy of 3D model generation for CMF deformities.

Main Methods:

  • A novel method employing patch-based sparse representation for automated CBCT segmentation.
  • Region-specific registration and sparse-based label propagation to create patient-specific atlases.
  • Integration of patient-specific atlases into a maximum a posteriori probability-based convex segmentation framework.

Main Results:

  • The proposed method demonstrated superior segmentation accuracy compared to traditional and state-of-the-art techniques.
  • Evaluation on 15 CBCT images validated the effectiveness of the region-specific registration and patient-specific atlas strategies.
  • The method successfully segmented bony structures and separated the mandible from the maxilla.

Conclusions:

  • A new CBCT segmentation method utilizing patch-based sparse representation and convex optimization was developed.
  • The proposed method achieves highly accurate segmentation results for CBCT images.
  • This technique offers a significant advancement for the diagnosis and treatment planning of CMF deformities.