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Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
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Image thresholding using fuzzy entropies.

S Di Zenzo1, L Cinque, S Levialdi

  • 1Dipt. di Sci. dell'Inf., Rome Univ.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 8, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces new fuzzy entropy measures for image analysis. These measures quantify image blur and are applied to image thresholding for cost minimization, improving image segmentation.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Fuzzy Set Theory

Background:

  • Images can be modeled as fuzzy subsets of a plane.
  • Fuzzy entropy quantifies image blur, increasing as image sharpness decreases.

Purpose of the Study:

  • To generalize the "sharper than" relation for fuzzy sets and images.
  • To construct novel fuzzy entropies for cost-minimizing image thresholding.

Main Methods:

  • Generalizing "sharper than" relation between fuzzy sets.
  • Developing and implementing fuzzy entropies for cost minimization.
  • Numerically solving minimization problems for image thresholding.

Main Results:

  • Infinitely many implementations of the "sharper than" relation were identified.
  • Constructed classes of fuzzy entropies useful for image thresholding.
  • Demonstrated effectiveness on a synthetic image.

Conclusions:

  • The developed fuzzy entropies are effective for image thresholding.
  • Fuzzy entropy provides a robust method for cost-minimization in image segmentation.
  • The generalized "sharper than" relation offers new insights into image sharpness comparison.