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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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Active contours driven by edge entropy fitting energy for image segmentation.

Lei Wang1,2, Guangqiang Chen3, Dai Shi3

  • 1Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.

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|July 11, 2019
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Summary
This summary is machine-generated.

This study introduces local edge entropy to improve active contour models for image segmentation. The novel approach enhances object delineation accuracy, especially in images with intensity inhomogeneity.

Keywords:
Active contour modelsImage segmentationIntensity inhomogeneityLocal edge entropy

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Area of Science:

  • Computer Vision
  • Image Processing
  • Medical Imaging

Background:

  • Active contour models are standard for image segmentation.
  • Intensity inhomogeneity can degrade segmentation performance.

Purpose of the Study:

  • To develop a novel active contour model robust to intensity inhomogeneity.
  • To introduce local edge entropy as a new image feature for segmentation.

Main Methods:

  • A new image feature, local edge entropy, was defined.
  • An active contour model using edge entropy fitting (EEF) energy was developed.
  • Variational level set formulation minimized the energy to guide contour evolution.

Main Results:

  • The proposed model effectively handles intensity inhomogeneity.
  • Experimental results show reasonable segmentation accuracy.
  • The model successfully delineates objects despite image variations.

Conclusions:

  • Local edge entropy is a valuable feature for improving active contour models.
  • The developed EEF model offers enhanced robustness and accuracy in image segmentation.
  • This method addresses a key limitation of traditional active contour techniques.