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Multi-threshold image segmentation for melanoma based on Kapur's entropy using enhanced ant colony optimization.

Xiao Yang1, Xiaojia Ye2, Dong Zhao3

  • 1School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, China.

Frontiers in Neuroinformatics
|November 17, 2022
PubMed
Summary

This study introduces a novel multi-threshold image segmentation model for melanoma pathology images. The enhanced ant colony optimization (EACOR) algorithm improves segmentation accuracy, providing high-quality samples for analysis.

Keywords:
Kapur’s entropyant colony algorithmmelanomamulti-threshold image segmentationswarm intelligence

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Area of Science:

  • Medical Imaging
  • Computational Pathology
  • Artificial Intelligence

Background:

  • Melanoma medical images contain crucial diagnostic information.
  • Challenges include limited critical information and non-uniform noise distribution.

Purpose of the Study:

  • To develop a novel multi-threshold image segmentation model for melanoma.
  • To enhance the accuracy of melanoma pathology image analysis.

Main Methods:

  • A two-dimensional histogram approach for image segmentation.
  • An enhanced ant colony optimization for continuous domains (EACOR) algorithm.
  • Integration of EACOR with two-dimensional Kapur's entropy for optimal thresholding.

Main Results:

  • The proposed EACOR algorithm demonstrated reliable global search capabilities.
  • Experimental results showed superior performance of the proposed segmentation model over comparison methods.
  • The model achieved higher scores in several evaluation metrics for image segmentation.

Conclusions:

  • The developed multi-threshold segmentation model effectively segments melanoma pathology images.
  • The model provides high-quality image samples essential for subsequent pathological analysis.
  • This approach has the potential to improve melanoma diagnosis and treatment planning.