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Related Concept Videos

Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

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.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
Diffusion01:12

Diffusion

Diffusion is the passive movement of substances down their concentration gradients—requiring no expenditure of cellular energy. Substances, such as molecules or ions, diffuse from an area of high concentration to an area of low concentration in the cytosol or across membranes. Eventually, the concentration will even out, with the substance moving randomly but causing no net change in concentration. Such a state is called dynamic equilibrium, which is essential for maintaining overall...
Entropy02:39

Entropy

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...
Debye–Huckel–Onsager Conductance Equation01:28

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The Debye-Hückel-Onsager equation is a cornerstone of physical chemistry, providing a method to determine the molar conductance (Λm) and molar conductance at infinite dilution (Λ°m) for uni-univalent electrolytes.Uni-univalent electrolytes are electrolytes that dissociate in solution to produce one cation with a +1 charge and one anion with a –1 charge per formula unit.This equation addresses two crucial phenomena: the asymmetry effect and the electrophoretic effect. According to this equation,...
Assessment of Diffusion and Perfusion01:17

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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

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Diffusion equations with negentropy applied to denoise mammographic images.

P Mayo1, F Rodenas, D Ginestar

  • 1Dept. of Chem. and Nucl. Eng., Politecnica Univ. of Valencia, Spain.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|October 20, 2007
PubMed
Summary

This study evaluates a novel diffusive filter for mammographic image denoising. The filter, using negentropy, effectively reduces noise while preserving diagnostic details, outperforming traditional Wiener filters.

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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Area of Science:

  • Medical Imaging
  • Image Processing
  • Radiography

Background:

  • Mammography is crucial for breast lesion detection.
  • Image noise reduction is essential for accurate mammogram analysis.
  • Existing denoising techniques vary in effectiveness.

Purpose of the Study:

  • To assess a diffusive filter for mammographic image denoising.
  • To evaluate the filter's performance using negentropy as a stopping criterion.
  • To compare its efficacy against adaptive non-linear Wiener filters.

Main Methods:

  • Application of a diffusive filter with negentropy-based stopping condition.
  • Denoising of digital mammographic images.
  • Quantitative evaluation using Root Mean Squared Error (RMSE).
  • Comparative analysis with an adaptive non-linear Wiener filter.

Main Results:

  • The diffusive filter successfully reduced noise in mammographic images.
  • Image quality was preserved, maintaining diagnostic details.
  • The negentropy-based filter demonstrated superior performance compared to the Wiener filter.

Conclusions:

  • The proposed diffusive filter is a promising technique for mammographic image denoising.
  • Negentropy provides an effective stopping criterion for noise reduction.
  • This method enhances diagnostic accuracy in mammography.