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Related Concept Videos

Structural Classification of Joints01:20

Structural Classification of Joints

9.0K
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...
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Functional Classification of Joints01:09

Functional Classification of Joints

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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...
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Related Experiment Video

Updated: Apr 18, 2026

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
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Multi-modal image registration using structural features.

Keyvan Kasiri, David A Clausi, Paul Fieguth

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 9, 2015
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel multi-modal image registration method using structural features, overcoming limitations of traditional intensity-based approaches. The new technique achieves comparable accuracy to mutual information for aligning magnetic resonance brain images.

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    Area of Science:

    • Medical Imaging
    • Computer Vision
    • Biomedical Engineering

    Background:

    • Multi-modal medical image registration is challenging due to complex inter-image intensity relationships.
    • Existing statistical intensity-based methods suffer from issues like statistical insufficiency.

    Purpose of the Study:

    • To propose a novel registration method that overcomes the limitations of traditional intensity-based approaches.
    • To improve the accuracy and robustness of multi-modal medical image alignment.

    Main Methods:

    • Extracting structural features using phase and gradient-based information.
    • Converting multi-modal registration into a mono-modal problem by leveraging structural relationships.
    • Utilizing conventional mono-modal similarity measures for evaluation.

    Main Results:

    • The proposed method was tested on multi-modal magnetic resonance (MR) brain images.
    • Registration accuracy was evaluated using target registration error (TRE).
    • Quantitative results showed comparable accuracy to conventional mutual information methods.

    Conclusions:

    • The novel registration paradigm effectively aligns multi-modal images by focusing on structural features.
    • This approach offers a robust alternative to intensity-based methods for medical image registration.
    • The method demonstrates significant potential for applications in medical image analysis and diagnostics.