Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 194, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2022.116511
Keywords
Medical image segmentation; Multi-threshold image segmentation; Shuffled frog leaping algorithm; Horizontal and vertical crossover search; Kapur's entropy; Image; Evolutionary algorithms; Optimization; Swarm intelligence
Categories
Funding
- Key Project of Zhejiang Provincial Natural Science Foundation [LD21F020001, LZ22F020005]
- National Natural Science Foundation of China [U19A2061, U1809209, 62076185]
- Key Laboratory of Intelligent Image Processing and Analysis, Wenzhou, China [2021HZSY0071]
- Taif University, Taif, Saudi Arabia [TURSP-2020/125]
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In this study, a multi-strategy-driven shuffled frog leaping algorithm with horizontal and vertical crossover search (HVSFLA) is proposed for medical image segmentation. The algorithm achieves a better balance between diversification and intensification through horizontal and vertical crossover search. Experimental results demonstrate that HVSFLA outperforms other competing algorithms, showing great potential for medical image segmentation.
Medical image segmentation, which is a complex and fundamental step in medical image processing, can help doctors make more precise decisions on patient diagnosis. Although multi-threshold image segmentation is the most exceptionally fundamental image segmentation technology, it requires complex computing and tends to yield unsatisfactory segmentation results, leading to its limited applications. To solve this problem, in this study, an ensemble multi strategy-driven shuffled frog leaping algorithm with horizontal and vertical crossover search (HVSFLA) is designed for multi-threshold image segmentation. Specifically, a horizontal crossover search enables different frogs to exchange information and guarantee the compelling exploration of each frog. Meanwhile, a vertical crossover search can make frogs in stagnation continue to search actively. Therefore, a better balance between diversification and intensification can be ensured. To evaluate its performance, HVSFLA was compared with a range of state-of-the-art algorithms using CEC 2017 benchmark functions. Furthermore, the performance of HVSFLA was also proved on several Berkeley segmentation datasets 500 (BSD5500). Finally, the proposed algorithm was applied to breast invasive ductal carcinoma cases based on multi-threshold segmentation technique using a non-local means 2D histogram integrated with Kapur's entropy. The experimental results demonstrate that the proposed HVSFLA outperforms a broad array of similar competitors, and thus it has a great potential to be used for medical image segmentation.
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