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- W4206602097 startingPage "116511" @default.
- W4206602097 abstract "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 (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." @default.
- W4206602097 created "2022-01-26" @default.
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- W4206602097 date "2022-05-01" @default.
- W4206602097 modified "2023-10-17" @default.
- W4206602097 title "Multi-threshold image segmentation using a multi-strategy shuffled frog leaping algorithm" @default.
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- W4206602097 cites W1975009952 @default.
- W4206602097 cites W1987234899 @default.
- W4206602097 cites W2007831275 @default.
- W4206602097 cites W2014743688 @default.
- W4206602097 cites W2028031385 @default.
- W4206602097 cites W2054131729 @default.
- W4206602097 cites W2061438946 @default.
- W4206602097 cites W2067715705 @default.
- W4206602097 cites W2083970667 @default.
- W4206602097 cites W2084648010 @default.
- W4206602097 cites W2097073572 @default.
- W4206602097 cites W2106905228 @default.
- W4206602097 cites W2114770744 @default.
- W4206602097 cites W2131613989 @default.
- W4206602097 cites W2133665775 @default.
- W4206602097 cites W2137340504 @default.
- W4206602097 cites W2141358266 @default.
- W4206602097 cites W2141983208 @default.
- W4206602097 cites W2157259291 @default.
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- W4206602097 cites W2806408894 @default.
- W4206602097 cites W2885736666 @default.
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- W4206602097 cites W2936227260 @default.
- W4206602097 cites W2940674768 @default.
- W4206602097 cites W2942406509 @default.
- W4206602097 cites W2950737312 @default.
- W4206602097 cites W2962004477 @default.
- W4206602097 cites W2974803302 @default.
- W4206602097 cites W2979491048 @default.
- W4206602097 cites W2980775892 @default.
- W4206602097 cites W2983298696 @default.
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- W4206602097 cites W3003416411 @default.
- W4206602097 cites W3007156934 @default.
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- W4206602097 cites W3016888800 @default.
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- W4206602097 cites W3082874886 @default.
- W4206602097 cites W3083641663 @default.
- W4206602097 cites W3085801332 @default.
- W4206602097 cites W3090332370 @default.
- W4206602097 cites W3093844748 @default.
- W4206602097 cites W3094509709 @default.
- W4206602097 cites W3094536368 @default.
- W4206602097 cites W3097900310 @default.
- W4206602097 cites W3121667743 @default.
- W4206602097 cites W3128499486 @default.
- W4206602097 cites W3134651880 @default.
- W4206602097 cites W3138874907 @default.
- W4206602097 cites W3154719286 @default.
- W4206602097 cites W3157424490 @default.
- W4206602097 cites W3157821078 @default.
- W4206602097 cites W3158130750 @default.
- W4206602097 cites W3160604150 @default.
- W4206602097 cites W3163936961 @default.
- W4206602097 cites W3169902780 @default.
- W4206602097 cites W3181491309 @default.
- W4206602097 cites W3185838909 @default.
- W4206602097 cites W3186841579 @default.
- W4206602097 cites W3199010270 @default.
- W4206602097 cites W3203894701 @default.
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- W4206602097 doi "https://doi.org/10.1016/j.eswa.2022.116511" @default.
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