DRPSO:A multi-strategy fusion particle swarm optimization algorithm with a replacement mechanisms for colon cancer pathology image segmentation

• Published in: Computers in biology and medicine Vol. 178; p. 108780
• Main Authors: Hu, Gang, Zheng, Yixuan, Houssein, Essam H., Wei, Guo
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.08.2024; Elsevier Limited
• Subjects: Adenocarcinoma; Adenocarcinoma - diagnostic imaging; Adenocarcinoma - pathology; Algorithms; Behavior; Biopsy; Colon adenocarcinoma; Colon cancer; Colonic Neoplasms - diagnostic imaging; Colonic Neoplasms - pathology; Colorectal cancer; Datasets; Deep learning; Diagnosis; Entropy (Information theory); Global optimization; Humans; Image enhancement; Image Interpretation, Computer-Assisted - methods; Image processing; Image Processing, Computer-Assisted - methods; Image quality; Image segmentation; Internal Medicine; Liver; Machine learning; Medical image segmentation; Medical imaging; Medical prognosis; Meta-heuristic algorithm; Methods; Multi-level thresholds; Mutation; Optimization algorithms; Other; Particle swarm optimization; Particle swarm optimization algorithm; Pathology; Patients; Performance measurement; Global optimization; Meta-heuristic algorithm; Colon adenocarcinoma; Particle swarm optimization algorithm; Medical image segmentation; Multi-level thresholds
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2024.108780

Colon adenocarcinoma (COAD) is a type of colon cancers with a high mortality rate. Its early symptoms are not obvious, and its late stage is accompanied by various complications that seriously endanger patients' lives. To assist in the early diagnosis of COAD and improve the detection efficiency of COAD, this paper proposes a multi-level threshold image segmentation (MIS) method based on an enhanced particle swarm algorithm for segmenting COAD images. Firstly, this paper proposes a multi-strategy fusion particle swarm optimization algorithm (DRPSO) with a replacement mechanism. The non-linear inertia weight and sine-cosine learning factors in DRPSO help balance the exploration and exploitation phases of the algorithm. The population reorganization strategy incorporating MGO enhances population diversity and effectively prevents the algorithm from stagnating prematurely. The mutation-based final replacement mechanism enhances the algorithm's ability to escape local optima and helps the algorithm to obtain highly accurate solutions. In addition, comparison experiments on the CEC2020 and CEC2022 test sets show that DRPSO outperforms other state-of-the-art algorithms in terms of convergence accuracy and speed. Secondly, by combining the non-local mean 2D histogram and 2D Renyi entropy, this paper proposes a DRPSO algorithm based MIS method, which is successfully applied to the segments the COAD pathology image problem. The results of segmentation experiments show that the above method obtains relatively higher quality segmented images with superior performance metrics: PSNR = 23.556, SSIM = 0.825, and FSIM = 0.922. In conclusion, the MIS method based on the DRPSO algorithm shows great potential in assisting COAD diagnosis and in pathology image segmentation. •A multi-strategy fusion particle swarm optimization algorithm (DRPSO) is proposed.•A DRPSO-based multi-level threshold segmentation method is proposed.•The optimization ability of the DRPSO is verified by CEC2020 and CEC2022 test sets.•Segmentation performance of the proposed method is tested on cervical cancer images.•The method in this paper significantly improves the quality of segmented images.