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Functional Classification of Joints

Functional Classification of Joints

Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
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Structural Classification of Joints

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Imaging Studies III: Computed Tomography

Imaging Studies III: Computed Tomography

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Related Experiment Video

Updated: May 25, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

JointMMCC: joint maximum-margin classification and clustering of imaging data.

Roman Filipovych¹, Susan M Resnick, Christos Davatzikos

¹Section of Biomedical ImageAnalysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA. roman.filipovych@uphs.upenn.edu

IEEE Transactions on Medical Imaging

|February 14, 2012

Summary

This study introduces a novel method to identify distinct patient groups within complex diseases using brain imaging. The approach helps understand disease heterogeneity and potentially tailor treatments for conditions like aging-related cognitive decline.

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Last Updated: May 25, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy

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Published on: November 28, 2025

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

Published on: October 27, 2023

Area of Science:

Neuroimaging
Computational Biology
Medical Data Analysis

Background:

Many conditions present as continuous spectra of pathological changes, from normal to severe.
Diseases are often heterogeneous, with subcategories within these spectra.
Identifying distinct subpopulations can improve understanding and treatment.

Purpose of the Study:

To develop a method for identifying coherent subpopulations within heterogeneous conditions with continuous pathological spectra.
To leverage magnetic resonance imaging (MRI) data for subpopulation discovery.
To address the challenge of disease heterogeneity in diagnostic and treatment strategies.

Main Methods:

Proposed a joint maximum-margin classification and clustering (JointMMCC) approach.
Utilized semi-supervised classification to detect pathological populations.
Employed a clustering subproblem to disentangle cohort heterogeneity.
Developed an efficient solution for the non-convex optimization problem.

Main Results:

Successfully applied the JointMMCC approach to a medical resonance imaging study of aging.
Identified coherent subpopulations (clusters) within the aging cohort.
These subpopulations represent cognitively less stable adults.

Conclusions:

The JointMMCC method effectively identifies distinct subpopulations in heterogeneous conditions with continuous pathological spectra.
This approach enhances understanding of disease heterogeneity using neuroimaging data.
Findings suggest potential for identifying specific groups needing tailored interventions, particularly in aging-related cognitive decline.