Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11889/8336
Title: Multi-threshold image segmentation using a multi-strategy shuffled frog leaping algorithm
Authors: Chen, Yi 
Wang, Mingjing 
Heidari, Ali Asghar 
Shi, Beibei 
Hu, Zhongyi 
Zhang, Qian 
Chen, Huiling 
Mafarja, Majdi 
Turabieh, Hamza 
Keywords: Diagnostic imaging - Data processing;Image segmentation;Diagnostic imaging - Digital techniques;Computer vision;Multi-threshold image segmentation;Shuffled frog leaping algorithm;Computer algorithms;Horizontal and vertical crossover search;Kapur’s entropy
Issue Date: 2022
Publisher: Expert Systems with Applications
Abstract: Medical image segmentation, 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 (BSDS500). 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.
URI: http://hdl.handle.net/20.500.11889/8336
DOI: 10.1016/j.eswa.2022.116511
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