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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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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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A living cell's primary tasks of obtaining, transforming, and using energy to do work may seem simple. However, the second law of thermodynamics explains why these tasks are harder than they appear. None of the energy transfers in the universe are completely efficient. In every energy transfer, some amount of energy is lost in a form that is unusable. In most cases, this form is heat energy. Thermodynamically, heat energy is defined as the energy transferred from one system to another that...
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The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
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Thresholding for Medical Image Segmentation for Cancer using Fuzzy Entropy with Level Set Algorithm.

Ismail Yaqub Maolood1, Yahya Eneid Abdulridha Al-Salhi1, Songfeng Lu1,2

  • 1School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.

Open Medicine (Warsaw, Poland)
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Summary

This study introduces a novel fuzzy entropy with level set (FELs) thresholding method for accurate cancer image segmentation. The FELs approach demonstrates superior performance in detecting cancer regions across various medical imaging modalities.

Keywords:
Fuzzy entropyImage segmentationLevel set algorithmThresholding cancer segmentation

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Area of Science:

  • Medical image analysis
  • Computational pathology
  • Biomedical imaging

Background:

  • Accurate cancer detection is crucial for effective treatment planning.
  • Existing medical image segmentation methods face challenges in precision and efficiency.
  • Developing robust segmentation techniques is vital for diverse imaging modalities.

Purpose of the Study:

  • To propose and validate a novel fuzzy entropy with level set (FELs) thresholding method for cancer image segmentation.
  • To evaluate the effectiveness of the FELs method across different medical image types.
  • To compare the performance of FELs against existing segmentation algorithms.

Main Methods:

  • Development of a new cancer segmentation technique based on fuzzy entropy and level set (FELs) thresholding.
  • Application and testing of the FELs method on ultrasound images, brain MRI, and dermoscopy images.
  • Comparative analysis of FELs performance with previously published algorithms.

Main Results:

  • The proposed FELs method achieved excellent performance in cancer region detection.
  • High accuracy, precision, specificity, and sensitivity were observed in segmentation results.
  • FELs demonstrated efficient and successful segmentation across ultrasound, MRI, and dermoscopy images.

Conclusions:

  • The fuzzy entropy with level set (FELs) thresholding method is an effective approach for cancer image segmentation.
  • FELs offers superior performance compared to existing methods for diverse medical imaging data.
  • This technique shows significant potential for improving cancer detection and diagnosis.