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Knee Joint

Knee Joint

The knee joint is the most complicated joint in the body. It consists of three articulations– two tibiofemoral and one patellofemoral. As is characteristic of synovial joints, the knee joint has a thin articular capsule that partially surrounds this joint cavity. Additionally, several ligaments, muscles, and cartilaginous structures support the movement of the knee.
A total of seven ligaments support the knee joint. The patellar ligament, which is also attached to the quadriceps femoris...

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Related Experiment Video

Updated: Apr 17, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

Automatic Articular Cartilage Segmentation Based on Pattern Recognition from Knee MRI Images.

Jianfei Pang¹, PengYue Li¹, Mingguo Qiu²

¹Department of Medical Image, College of Biomedical Engineering, Third Military Medical University, Chongqing, China.

Journal of Digital Imaging

|February 22, 2015

Summary

This study presents an automated method for segmenting knee cartilage in MRI images using Bayesian classifiers. The approach achieves accurate and consistent results, comparable to manual segmentation.

Keywords:

Articular cartilage Knee MRI Pattern recognition Segmentation

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Last Updated: Apr 17, 2026

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Published on: November 28, 2025

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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-aided diagnosis

Background:

Accurate cartilage segmentation in knee MRI is crucial for diagnosing conditions like osteoarthritis.
Manual segmentation is time-consuming and prone to inter-observer variability.

Purpose of the Study:

To develop and validate an automatic method for segmenting femoral, tibial, and patellar cartilage in knee MRI images.
To improve the efficiency and consistency of knee cartilage segmentation.

Main Methods:

Utilized three binary Bayesian classifiers with pixel features for segmenting different knee cartilages.
Employed an iterative Canny edge detection with feedback for bone-cartilage interface extraction.
Incorporated feature-based edge identification and morphological operations for refinement.

Main Results:

The automatic method achieved a mean Dice similarity coefficient of 0.761.
Demonstrated good consistency with manual segmentation results.
Produced smooth cartilage edges in the segmentation output.

Conclusions:

The developed automatic method offers an efficient and reliable approach for knee cartilage segmentation.
This technique has the potential to aid in the quantitative assessment of knee joint health.
The method shows promising results for clinical applications in orthopedic imaging.