Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

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

The Preparation of C-Reactive Protein Immunosensor Based on Nano-Mimetic Enzyme Co₃O₄.

Journal of biomedical nanotechnology·2018

Same author

Study on a Novel Binary Zn <sub>n</sub> Eu Layered Double Hydroxide with Excellent Fluorescence.

Journal of fluorescence·2017

Same author

The application of a new sampling theorem for non-bandlimited signals on the sphere: Improving the recovery of crossing fibers for low b-value acquisitions.

Medical image analysis·2016

Same author

Echogenicity based approach to detect, segment and track the common carotid artery in 2D ultrasound images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2016

Same author

Speckle Patch Similarity for Echogenicity-Based Multiorgan Segmentation in Ultrasound Images of the Thyroid Gland.

IEEE journal of biomedical and health informatics·2015

Same author

Relationship Between Peripapillary Choroid and Retinal Nerve Fiber Layer Thickness in a Population-Based Sample of Nonglaucomatous Eyes.

American journal of ophthalmology·2015

Same journal

Incremental diagnostic value of microstructural time-dependent diffusion MRI in differentiating PCNSL from glioblastoma over conventional MRI.

Magnetic resonance imaging·2026

Same journal

Enhanced motion compensation for free-breathing dynamic contrast-enhanced MRI with GROG-facilitated bunch phase encoding and Golden angle radial sampling.

Magnetic resonance imaging·2026

Same journal

The allegory of the cave: 10 years of AI shadows in radiology.

Magnetic resonance imaging·2026

Same journal

Conversion of 3 T liver, spleen, pancreas, and kidney R2* measurements to 1.5 T R2* equivalents: Validation of a theoretical framework.

Magnetic resonance imaging·2026

Same journal

Cine-derived mitral annular relaxation velocity for detection of preclinical left ventricular diastolic dysfunction.

Magnetic resonance imaging·2026

Same journal

Bone marrow fat fraction and R2* in sickle cell disease: Associations with hemolysis, iron metabolism, and disease severity.

Magnetic resonance imaging·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 9, 2026

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

Automatic knee cartilage segmentation from multi-contrast MR images using support vector machine classification with

Kunlei Zhang¹, Wenmiao Lu, Pina Marziliano

¹School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore.

Magnetic Resonance Imaging

|July 23, 2013

Summary

This summary is machine-generated.

This study introduces an automatic knee cartilage segmentation method using multi-contrast MRI features and spatial dependencies. The novel approach achieves superior accuracy for quantitative cartilage assessment in knee pathology.

Keywords:

Discriminative random field (DRF)Knee cartilage Magnetic resonance imaging (MRI)Multi-contrast Segmentation Support vector machine (SVM)

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

Related Experiment Videos

Last Updated: Jan 9, 2026

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

Area of Science:

Medical Imaging
Biomedical Engineering
Computer Vision

Background:

Accurate knee cartilage segmentation is vital for quantitative measurements in assessing knee pathology from musculoskeletal diseases and injuries.
Existing methods may lack the precision required for reliable quantitative analysis of knee cartilage.
Multi-contrast Magnetic Resonance (MR) imaging offers rich information for detailed cartilage analysis.

Purpose of the Study:

To develop an automatic knee cartilage segmentation technique utilizing multi-contrast MR images.
To improve the accuracy and efficiency of knee cartilage segmentation for quantitative measurements.
To enhance the assessment of knee pathology through precise cartilage analysis.

Main Methods:

Exploited diverse image features from multi-contrast MR images and spatial dependencies between voxels.
Modeled features and dependencies into Support Vector Machine (SVM)-based association potential and Discriminative Random Field (DRF)-based interaction potential.
Integrated potentials into a graphical model solved via loopy belief propagation for optimal labeling.

Main Results:

The joint SVM-DRF model demonstrated superior accuracy compared to models using only DRF or SVM with identical features.
Incorporating diverse image and anatomical structure information significantly improved segmentation performance.
The developed technique achieved high performance, surpassing state-of-the-art automatic cartilage segmentation studies with higher average Dice Similarity Coefficient (DSC) values.

Conclusions:

The proposed automatic knee cartilage segmentation technique effectively utilizes multi-contrast MR imaging and spatial information.
The combined SVM-DRF model offers a robust and accurate approach for knee cartilage segmentation.
This method provides a valuable tool for quantitative cartilage assessment and the diagnosis of knee pathologies.