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Structural Classification of Joints

Structural Classification of Joints

Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...

Functional Classification of Joints

Functional Classification of Joints

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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.
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Classification of Bones

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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.
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The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The...

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Related Experiment Video

Updated: Dec 31, 2025

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

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Published on: January 18, 2020

Organ Localization Using Joint AP/LAT View Landmark Consensus Detection and Hierarchical Active Appearance Models.

Qi Song¹, Albert Montillo¹, Roshni Bhagalia¹

¹General Electric Global Research, Niskayuna, NY, USA.

Medical Computer Vision : Large Data in Medical Imaging : Third International MICCAI Workshop, MCV 2013, Nagoya, Japan, September 26, 2013 : Revised Selected Papers. MCV (Workshop) (3Rd : 2013 : Nagoya-Shi, Japan)

|January 10, 2020

Summary

This summary is machine-generated.

This study introduces a novel method for precisely locating anatomical landmarks in 2D X-ray images by integrating anterior-posterior (AP) and lateral (LAT) views. This approach significantly improves landmark detection accuracy for clinical applications.

Keywords:

Automatic landmark localization CT Hierarchical active appearance model Image parsing Organ localization Rejection cascade

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Published on: May 7, 2019

Area of Science:

Medical Imaging
Radiography Analysis
Computational Anatomy

Background:

Accurate parsing of 2D radiographs into anatomical regions is crucial for clinical applications.
Anterior-posterior (AP) and lateral (LAT) views offer complementary information for enhanced localization.
Current methods struggle with integrating information from multiple radiographic views effectively.

Purpose of the Study:

To develop an automated method for joint analysis of AP and LAT radiographs to improve anatomical landmark localization.
To enhance the accuracy and robustness of landmark detection by integrating complementary view information.
To reduce landmark detection errors for clinical applications like personalized scan planning.

Main Methods:

Proposed a probabilistic joint consensus detection model for predicting landmark locations across views.
Refined landmark detection using a joint hierarchical active appearance organ model (H-AAM) integrating landmark arrangement and image appearance.
Developed a method that requires only seconds for computation.

Main Results:

Joint processing reduced mean landmark distance error from 27.3 mm to 15.7 mm in LAT view and from 12.7 mm to 11.2 mm in AP view.
Achieved landmark detection errors comparable to human expert inter-observer variability.
Demonstrated robustness to anatomical variation using a database of 93 subjects with diverse pathologies.

Conclusions:

The proposed joint analysis method significantly improves anatomical landmark localization accuracy in 2D radiographs.
This technique offers a robust and efficient solution for integrating complementary radiographic views.
The findings support the clinical utility of this method for applications such as personalized dose reduction in radiation therapy.