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<p>Breast cancer is a cancer with a high mortality rate in the medical field. Its pathological images exhibit considerable complexity, characterized by diverse intensity and color patterns in tissue structures and lesion areas. This diversity presents challenges for traditional segmentation techniques in accurately identifying subtle features. In contrast, the multi-threshold image segmentation method optimizes the threshold selection process using meta-heuristic algorithms, enabling more precise segmentation of various tissue types and lesion areas. Therefore, this study introduces a multi-threshold image segmentation model utilizing a modified ant colony optimization algorithm for continuous domains (ACOR), with the goal of enhancing the effectiveness of segmenting breast cancer pathology images. By integrating the Worm Strategy (WS) and Generalized Quadratic Interpolation (GQI) into ACOR, a novel optimization algorithm named GWACOR is developed. Specifically, WS facilitates individuals in jumping out of local optima, thereby bolstering the algorithm’s global search capabilities. GQI strengthens the local exploitation ability of individuals, further leading to enhanced solution accuracy. To evaluate the optimization performance of GWACOR, a series of comparative experiments are carried out on IEEE CEC2017 across three different dimensions. The experimental results show that GWACOR surpasses comparable algorithms in overall performance. Further, a multi-threshold image segmentation model based on GWACOR (GWACOR-MTIS) is formulated by amalgamating GWACOR with the non-local mean two-dimensional histogram and Kapur’s entropy. Through segmentation and comparison with various competing models across different threshold levels, the results demonstrate that GWACOR-MTIS possesses a competitive advantage in segmentation accuracy, providing potentially valuable and dependable diagnostic assistance for clinicians. Consider that the source code of GWACOR is publicly available at <a href="https://github.com/EnhancedAlgorithms/GWACOR">https://github.com/EnhancedAlgorithms/GWACOR</a>.</p>

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A multi-threshold image segmentation model for breast cancer based on enhanced ant colony algorithm by integrating wormhole strategy and generalized quadratic interpolation

  • 期刊论文

  • 发表日期：

    2026年4月1日

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  • Liang Zuo,
  • Dong Zhao,
  • Helong Yu,
  • Ali Asghar Heidari,
  • Lei Liu,
  • Qike Shao,
  • Yi Chen,
  • Huiling Chen

摘要

Breast cancer is a cancer with a high mortality rate in the medical field. Its pathological images exhibit considerable complexity, characterized by diverse intensity and color patterns in tissue structures and lesion areas. This diversity presents challenges for traditional segmentation techniques in accurately identifying subtle features. In contrast, the multi-threshold image segmentation method optimizes the threshold selection process using meta-heuristic algorithms, enabling more precise segmentation of various tissue types and lesion areas. Therefore, this study introduces a multi-threshold image segmentation model utilizing a modified ant colony optimization algorithm for continuous domains (ACOR), with the goal of enhancing the effectiveness of segmenting breast cancer pathology images. By integrating the Worm Strategy (WS) and Generalized Quadratic Interpolation (GQI) into ACOR, a novel optimization algorithm named GWACOR is developed. Specifically, WS facilitates individuals in jumping out of local optima, thereby bolstering the algorithm’s global search capabilities. GQI strengthens the local exploitation ability of individuals, further leading to enhanced solution accuracy. To evaluate the optimization performance of GWACOR, a series of comparative experiments are carried out on IEEE CEC2017 across three different dimensions. The experimental results show that GWACOR surpasses comparable algorithms in overall performance. Further, a multi-threshold image segmentation model based on GWACOR (GWACOR-MTIS) is formulated by amalgamating GWACOR with the non-local mean two-dimensional histogram and Kapur’s entropy. Through segmentation and comparison with various competing models across different threshold levels, the results demonstrate that GWACOR-MTIS possesses a competitive advantage in segmentation accuracy, providing potentially valuable and dependable diagnostic assistance for clinicians. Consider that the source code of GWACOR is publicly available at https://github.com/EnhancedAlgorithms/GWACOR.