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Related Concept Videos

Functional Classification of Joints01:09

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 immobile...
Structural Classification of Joints01:20

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 Connective Tissues01:30

Classification of Connective Tissues

The connective tissues have different properties and functions in the human body. They are broadly categorized into proper, supporting, or fluid connective tissues.
Connective Tissue Proper
Connective tissue proper is the most abundant class of connective tissues. As its name implies, it predominantly connects different tissues in the body. Depending on the cell types, ground substance, viscosity, and fiber types in the ECM, connective tissue proper is further categorized into loose and dense.
Classification of Bones01:18

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 long...
Knee Joint01:23

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 group...

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Related Experiment Video

Updated: May 13, 2026

Standardized Histomorphometric Evaluation of Osteoarthritis in a Surgical Mouse Model
07:32

Standardized Histomorphometric Evaluation of Osteoarthritis in a Surgical Mouse Model

Published on: May 6, 2020

Osteoarthritis classification using self organizing map based on gabor kernel and contrast-limited adaptive histogram

Lilik Anifah1, I Ketut Eddy Purnama, Mochamad Hariadi

  • 1Electrical Engineering Department, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia ; Electrical Engineering Department, Universitas Negeri Surabaya, Indonesia.

The Open Biomedical Engineering Journal
|March 26, 2013
PubMed
Summary

This study introduces an automated system for classifying knee osteoarthritis (OA) severity, improving upon manual methods. The system achieves high accuracy in grading osteoarthritis, aiding clinical decision-making.

Keywords:
Contrast Limited Adaptive Histogram Equalization (CLAHE)Gabor kernel.Knee osteoarthritisSelf Organizing Map (SOM)classificationgray tone spatial dependency matrix (GLCM)

Related Experiment Videos

Last Updated: May 13, 2026

Standardized Histomorphometric Evaluation of Osteoarthritis in a Surgical Mouse Model
07:32

Standardized Histomorphometric Evaluation of Osteoarthritis in a Surgical Mouse Model

Published on: May 6, 2020

Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Osteoarthritis Research

Background:

  • Manual classification of knee osteoarthritis (OA) severity is labor-intensive and costly.
  • Accurate OA classification is crucial for effective treatment planning.
  • Automated systems can enhance efficiency and consistency in OA diagnosis.

Purpose of the Study:

  • To develop a decision support system for classifying knee OA severity.
  • To automate the localization and classification of joint spaces in knee radiographs.
  • To categorize OA into Kellgren-Lawrence (KL) Grades 0-4.

Main Methods:

  • Image preprocessing using Contrast-Limited Adaptive Histogram Equalization (CLAHE) for intensity normalization.
  • Joint space localization via Gabor kernel, row sum graph, and moment methods.
  • Segmentation using Gabor kernel, template matching, row sum graph, and gray level center of mass.
  • Feature extraction using Gray-Level Co-occurrence Matrix (GLCM) properties (contrast, correlation, energy, homogeneity).
  • Classification using a trained model with specific Gabor kernel parameters and optimization settings.

Main Results:

  • The system demonstrated high classification accuracy for KL-Grade 0 (93.8%) and KL-Grade 4 (88.9%).
  • Achieved 70% accuracy for KL-Grade 1, with lower accuracies for KL-Grade 2 (4%) and KL-Grade 3 (10%).
  • The Gabor kernel method with optimized parameters yielded the best performance.

Conclusions:

  • The proposed automated system shows potential for efficient and accurate knee OA severity classification.
  • Further refinement may be needed to improve classification accuracy for intermediate OA grades.
  • This decision support tool can assist medical professionals in OA diagnosis.