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The Application of Tsallis Entropy Based Self-Adaptive Algorithm for Multi-Threshold Image Segmentation.

Kailong Zhang1, Mingyue He1, Lijie Dong1

  • 1College of Information Science and Engineering, Huaqiao University, Xiamen 361021, China.

Entropy (Basel, Switzerland)
|September 27, 2024
PubMed
Summary
This summary is machine-generated.

This study explores Tsallis entropy for multi-threshold image segmentation, linking the parameter q to pixel correlations. Adaptive algorithms reveal stable correlations in specific image types, aiding unsupervised processing.

Keywords:
long-range correlationsmulti-level thresholdingrobustnessself-adaptive algorithmtsallis entropy

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Area of Science:

  • Image processing and analysis
  • Non-extensive statistical mechanics
  • Information theory

Background:

  • Tsallis entropy is utilized in image thresholding due to its non-extensive properties.
  • The non-extensive parameter q is crucial for adaptive algorithms in bi-level image thresholding.

Purpose of the Study:

  • Investigate the relationship between parameter q and pixel long-range correlations in multi-threshold image segmentation.
  • Evaluate the effectiveness of a self-adaptive Tsallis entropy algorithm for parameter q estimation.
  • Compare the proposed algorithm with Shannon entropy and original Tsallis entropy methods using image quality metrics.

Main Methods:

  • Developed a self-adaptive Tsallis entropy algorithm to evaluate parameter q.
  • Studied multi-threshold image segmentation focusing on parameter q and pixel correlations.
  • Quantitatively assessed image quality using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM).

Main Results:

  • Identified remarkable and stable pixel correlations in images from known physical principles (infrared, CT, satellite).
  • The adaptive algorithm consistently determined q values within a narrow range for images with similar backgrounds.
  • The proposed method showed comparable or improved results against Shannon and original Tsallis entropy algorithms.

Conclusions:

  • Parameter q reflects long-range pixel correlations in specific image types.
  • Consistent q values for similar scenes suggest potential for unsupervised image processing applications.
  • The self-adaptive Tsallis entropy algorithm offers a robust approach for image segmentation and analysis.