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Related Experiment Video

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Changes in Mammary Gland Morphology and Breast Cancer Risk in Rats
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Fuzzy entropy based on differential evolution for breast gland segmentation.

Yuling Fan1, Peizhong Liu2, Jianeng Tang1,3

  • 1College of Engineering, Huaqiao University, No. 269, Chenghua North Road, Quanzhou, 362021, Fujian, China.

Australasian Physical & Engineering Sciences in Medicine
|September 12, 2018
PubMed
Summary

This study introduces a novel automatic segmentation algorithm for mammary glands using differential evolution (DE) fuzzy entropy. The method achieves high accuracy in segmenting breast tissue, crucial for effective breast tumor diagnosis and treatment.

Keywords:
Differential evolutionImage segmentationMaximum fuzzy entropyThreshold method

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Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Biomedical Engineering

Background:

  • Accurate segmentation of mammary glands is essential for effective diagnosis and treatment planning of breast tumors.
  • Current methods may lack the precision required for optimal patient outcomes.

Purpose of the Study:

  • To develop and evaluate an automatic segmentation algorithm for mammary glands based on differential evolution (DE) fuzzy entropy.
  • To improve the accuracy and robustness of mammary gland segmentation for breast tumor analysis.

Main Methods:

  • Constructed an image segmentation evaluation function using image fuzzy entropy.
  • Employed differential evolution (DE) algorithm to optimize fuzzy entropy parameters for threshold determination.
  • Segmented mammary glands using the maximum fuzzy entropy threshold method.

Main Results:

  • The proposed algorithm demonstrated high accuracy (Acc: 98.46% ± 8.02E-03%) and structural similarity (Mssim: 0.985).
  • Achieved excellent performance across various metrics including sensitivity, specificity, PPV, and NPV.
  • Outperformed other fuzzy entropy-based swarm intelligent optimization algorithms in robustness and effectiveness.

Conclusions:

  • The differential evolution fuzzy entropy algorithm provides highly accurate mammary gland segmentation.
  • This method shows potential as a gold standard for breast tumor analysis and treatment planning.
  • The algorithm's effectiveness and robustness support its application in clinical settings.