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Related Concept Videos

Functional Classification of Joints01:09

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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
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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.
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The divergence and Stokes' theorems are a variation of Green's theorem in a higher dimension. They are also a generalization of the fundamental theorem of calculus. The divergence theorem and Stokes' theorem are in a way similar to each other; The divergence theorem relates to the dot product of a vector, while Stokes' theorem relates to the curl of a vector. Many applications in physics and engineering make use of the divergence and Stokes' theorems, enabling us to write...
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The Region of Convergence (ROC) is a fundamental concept in signal processing and system analysis, particularly associated with the Laplace transform. The ROC represents an area in the complex plane where the Laplace transform of a given signal converges, determining the transform's applicability and utility.
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The z-transform is a powerful mathematical tool used in the analysis of discrete-time signals and systems. It is a crucial tool in the analysis of discrete-time systems, but its convergence is limited to specific values of the complex variable z. This range of values, known as the Region of Convergence (ROC), is fundamental in determining the behavior and stability of a system or signal. The ROC defines the region in the complex plane where the z-transform converges, which can take various...
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Divergence and Curl01:15

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The divergence of a vector field at a point is the net outward flow of the flux out of a small volume through a closed surface enclosing the volume, as the volume tends to zero. More practically, divergence measures how much a vector field spreads out or diverges from a given point. For an outgoing flux, conventionally, the divergence is positive. The diverging point is often called the "source" of the field. Meanwhile, the negative divergence of a vector field at a point means that the vector...
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Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
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SU-E-J-110: A Novel Level Set Active Contour Algorithm for Multimodality Joint Segmentation/Registration Using the

D Markel1, I El Naqa1, C Freeman1

  • 1McGill University, Montreal, QC.

Medical Physics
|May 19, 2017
PubMed
Summary

This study introduces Jensen-Renyi (JR) divergence for robust joint segmentation and registration in radiotherapy imaging. The novel approach significantly outperforms traditional methods in noisy, multi-modality environments.

Keywords:
EntropyImage analysisImage registrationMedical image segmentationMedical imagingRadiation therapy

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Area of Science:

  • Medical Imaging
  • Radiotherapy
  • Image Analysis

Background:

  • Multimodality image-guided and adaptive radiotherapy require robust segmentation and registration.
  • Existing algorithms are often sensitive to noise, limiting their effectiveness.

Purpose of the Study:

  • To present a novel joint segmentation/registration framework for radiotherapy.
  • To improve noise robustness in multimodality imaging using Jensen-Renyi (JR) divergence.

Main Methods:

  • Developed a level set active contour model utilizing Jensen-Renyi (JR) divergence.
  • Applied the model for joint segmentation and registration in a multi-modality imaging space.

Main Results:

  • JR divergence demonstrated superior noise robustness compared to mutual information (MI) and other entropy-based metrics.
  • The MI metric failed at approximately 2/3 the noise power level where JR divergence remained effective.

Conclusions:

  • JR divergence is a valuable metric for joint segmentation/registration of multimodality images, outperforming entropy-based metrics.
  • The algorithm's adaptability allows for incorporation of non-intensity based images, enabling broader applications in texture analysis.