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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
An...

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...

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...

Classification of Bones

Classification of Bones

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...

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Related Experiment Video

Updated: Nov 19, 2025

Standardized Histomorphometric Evaluation of Osteoarthritis in a Surgical Mouse Model

Standardized Histomorphometric Evaluation of Osteoarthritis in a Surgical Mouse Model

Published on: May 6, 2020

Automatic Detection and Classification of Knee Osteoarthritis Using Hu's Invariant Moments.

Shivanand S Gornale¹, Pooja U Patravali¹, Prakash S Hiremath²

¹Department of Computer Science, Rani Channamma University, Belagavi, India.

Frontiers in Robotics and AI

|January 27, 2021

Summary

Hu's invariant moments enable early detection and grading of knee osteoarthritis by analyzing geometric transformations in X-ray images. This method offers promising results validated by medical experts.

Keywords:

Hu's invariant moments K-NN KL grading knee radiography osteoarthritis (OA)

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Last Updated: Nov 19, 2025

Standardized Histomorphometric Evaluation of Osteoarthritis in a Surgical Mouse Model

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Published on: May 6, 2020

Software-Assisted Quantitative Measurement of Osteoarthritic Subchondral Bone Thickness

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Published on: March 18, 2022

Destabilization of the Medial Meniscus and Cartilage Scratch Murine Model of Accelerated Osteoarthritis

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Published on: July 6, 2022

Area of Science:

Medical Imaging
Image Processing
Biomedical Engineering

Background:

Geometric distortions in X-ray images complicate information extraction.
Accurate analysis of cartilage region is crucial for diagnosing knee osteoarthritis.

Purpose of the Study:

To utilize Hu's invariant moments for early detection and grading of knee osteoarthritis.
To analyze geometric transformations in the knee cartilage region of X-ray images.

Main Methods:

Computed seven invariant moments for rotated knee X-ray images.
Applied Hu's invariant moments to understand geometric transformations of the cartilage.
Validated findings with orthopedic surgeons and rheumatologists.

Main Results:

Demonstrated competitive and promising results in knee osteoarthritis detection and grading.
Hu's moments effectively captured geometric transformations in distorted images.
Expert validation confirmed the clinical relevance of the findings.

Conclusions:

Hu's invariant moments are effective for analyzing geometrically transformed medical images.
This technique shows potential for early and accurate knee osteoarthritis diagnosis.
The method provides a valuable tool for radiologists and clinicians.