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Related Concept Videos

Structural Classification of Joints01:20

Structural Classification of Joints

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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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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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Entropy01:18

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The first law of thermodynamics is quantitatively formulated via an equation relating the internal energy of a system, the heat exchanged by it, and the work done on it. A quantitative formulation of the second law of thermodynamics leads to defining a state function, the entropy.
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Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
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In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
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Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
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Related Experiment Video

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Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
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Multimodal image registration with joint structure tensor and local entropy.

Jingya Zhang1,2, Jiajun Wang3, Xiuying Wang4

  • 1School of Electronic and Information Engineering, Soochow University, Suzhou, 215006, People's Republic of China. zhangjy0611@163.com.

International Journal of Computer Assisted Radiology and Surgery
|May 29, 2015
PubMed
Summary

This study introduces a novel two-stage multimodal nonrigid registration method. By integrating structural tensor (ST) and local entropy (LE), it significantly improves registration accuracy over traditional mutual information (MI) methods.

Keywords:
Local entropyMutual informationNonrigid registrationStructure tensor

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Area of Science:

  • Medical image analysis
  • Computer vision
  • Biomedical engineering

Background:

  • Nonrigid registration of multimodal medical images is crucial for image-guided interventions.
  • Mutual information (MI) is a common but limited approach due to its focus on intensity distributions, potentially causing local optimization.
  • Existing methods often fail to fully utilize spatial and structural information inherent in medical images.

Purpose of the Study:

  • To develop a robust two-stage multimodal nonrigid registration scheme that incorporates joint structural information and local entropy.
  • To overcome the limitations of intensity-based methods like MI by leveraging geometric and structural image properties.
  • To enhance the accuracy and reliability of medical image registration across different modalities.

Main Methods:

  • A two-stage registration process where images are converted to a common space.
  • A unified image representation is created by fusing the structure tensor (ST) trace with local entropy (LE).
  • Deformation fields are estimated using L(1) or L(2) distance based on this unified representation.

Main Results:

  • The proposed method demonstrated superior performance compared to LE-only, ST-only, spatially weighted LE, and conventional MI-based methods.
  • Quantitative evaluations on 80 multimodal image pairs (MR, brain MR, breast images) confirmed the method's effectiveness.
  • Statistical analysis (Student's t test) indicated a significant improvement in registration accuracy.

Conclusions:

  • The two-stage registration approach combining ST and LE offers a significant advancement over traditional MI-based methods for multimodal image registration.
  • Both structural tensor (ST) and local entropy (LE) individually and jointly contribute to enhanced registration accuracy.
  • This method provides a more robust solution for challenging nonrigid registration tasks in medical imaging.