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Colored Texture Analysis Fuzzy Entropy Methods with a Dermoscopic Application.

Mirvana Hilal1, Andreia S Gaudêncio1,2, Pedro G Vaz2

  • 1Univ Angers, LARIS, SFR MATHSTIC, F-49000 Angers, France.

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|June 24, 2022
PubMed
Summary
This summary is machine-generated.

New fuzzy entropy methods (FuzEnC2D, FuzEnV2D, FuzEnM2D) analyze colored textures. These novel approaches show promise in distinguishing between similar skin lesions, outperforming existing methods.

Keywords:
colored texture analysisdermoscopyentropyfuzzy entropyinformation theorymedical image analysismelanomatexture analysis

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Area of Science:

  • Image Processing
  • Information Theory
  • Medical Imaging

Background:

  • Texture analysis is crucial in image processing, but colored texture analysis using information theory is underdeveloped.
  • Existing entropy measures are primarily for grayscale images, lacking robust methods for color images.

Purpose of the Study:

  • To introduce novel fuzzy entropy measures for colored texture analysis.
  • To evaluate the performance of these new methods in distinguishing image textures and classifying skin lesions.

Main Methods:

  • Proposed FuzEnC2D (colored bidimensional fuzzy entropy) and its multi-channel extensions, FuzEnV2D and FuzEnM2D.
  • Investigated parameter sensitivity and performance on Brodatz images across different color spaces.
  • Applied methods to differentiate dermoscopic images of malignant melanoma and benign melanocytic nevi.

Main Results:

  • The proposed fuzzy entropy measures (FuzEnC2D, FuzEnV2D, FuzEnM2D) demonstrated effectiveness in analyzing colored textures.
  • These methods showed significant ability to differentiate between malignant melanoma and benign melanocytic nevi.
  • Performance surpassed a well-established texture analysis measure.

Conclusions:

  • FuzEnC2D, FuzEnV2D, and FuzEnM2D represent the first entropy measures for colored images using single and multi-channel approaches.
  • These methods offer a valuable tool for texture analysis in color images, particularly in medical diagnostics.