Matches in SemOpenAlex for { <https://semopenalex.org/work/W3211639065> ?p ?o ?g. }
- W3211639065 endingPage "105015" @default.
- W3211639065 startingPage "105015" @default.
- W3211639065 abstract "Multi-threshold image segmentation (MIS) is now a well known image segmentation technique, and many researchers have applied intelligent algorithms to it, but these methods suffer from local optimal drawbacks. This paper presented a novel approach to improve the Salp Swarm Algorithm (SSA), namely EHSSA, and applied it to MIS. Knowing the inaccuracies and discussions on implementation of this method, a new efficient mechanism is proposed to improve global search capability of the algorithm and avoid falling into a local optimum. Moreover, the excellence of the proposed algorithm was proved by comparative experiments at IEEE CEC2014. Afterward, the performance of EHSSA was demonstrated by testing a set of images selected from the Berkeley segmentation data set 500 (BSDS500), and the experimental results were analyzed by evaluating the parameters, which proved the efficiency of the proposed algorithm in MIS. Furthermore, EHSSA was applied to the microscopic image segmentation of breast cancer. Medical image segmentation is the study of how to quickly extract objects of interest (human organs) from various images to perform qualitative and quantitative analysis of diseased tissues and improve the accuracy of their diagnosis, which assists the physician in making more informed decisions and patient rehabilitation. The results of this set of experiments also proved its superior performance. For any info about this paper, readers can refer to https://aliasgharheidari.com." @default.
- W3211639065 created "2021-11-22" @default.
- W3211639065 creator A5024138459 @default.
- W3211639065 creator A5025935227 @default.
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- W3211639065 creator A5033271171 @default.
- W3211639065 creator A5058470689 @default.
- W3211639065 creator A5082892026 @default.
- W3211639065 date "2021-12-01" @default.
- W3211639065 modified "2023-10-14" @default.
- W3211639065 title "Performance optimization of salp swarm algorithm for multi-threshold image segmentation: Comprehensive study of breast cancer microscopy" @default.
- W3211639065 cites W1811310034 @default.
- W3211639065 cites W1950229535 @default.
- W3211639065 cites W1970271356 @default.
- W3211639065 cites W2007831275 @default.
- W3211639065 cites W2012459362 @default.
- W3211639065 cites W2081873429 @default.
- W3211639065 cites W2084581746 @default.
- W3211639065 cites W2085514593 @default.
- W3211639065 cites W2112550675 @default.
- W3211639065 cites W2114770744 @default.
- W3211639065 cites W2117533322 @default.
- W3211639065 cites W2131613989 @default.
- W3211639065 cites W2133665775 @default.
- W3211639065 cites W2141358266 @default.
- W3211639065 cites W2141983208 @default.
- W3211639065 cites W2146713522 @default.
- W3211639065 cites W2151554678 @default.
- W3211639065 cites W2152195021 @default.
- W3211639065 cites W2156999391 @default.
- W3211639065 cites W2163848045 @default.
- W3211639065 cites W2165466912 @default.
- W3211639065 cites W2168081761 @default.
- W3211639065 cites W2176872390 @default.
- W3211639065 cites W2232748179 @default.
- W3211639065 cites W2404179621 @default.
- W3211639065 cites W2468812405 @default.
- W3211639065 cites W2518812438 @default.
- W3211639065 cites W2589765509 @default.
- W3211639065 cites W2613771876 @default.
- W3211639065 cites W2615743202 @default.
- W3211639065 cites W2617638177 @default.
- W3211639065 cites W2732796807 @default.
- W3211639065 cites W2738900493 @default.
- W3211639065 cites W2780800659 @default.
- W3211639065 cites W2792689221 @default.
- W3211639065 cites W2793992319 @default.
- W3211639065 cites W2801410648 @default.
- W3211639065 cites W2801536506 @default.
- W3211639065 cites W2802292836 @default.
- W3211639065 cites W2806408894 @default.
- W3211639065 cites W2808701618 @default.
- W3211639065 cites W2809169054 @default.
- W3211639065 cites W2883013658 @default.
- W3211639065 cites W2887171350 @default.
- W3211639065 cites W2889803518 @default.
- W3211639065 cites W2898634570 @default.
- W3211639065 cites W2899250423 @default.
- W3211639065 cites W2901864827 @default.
- W3211639065 cites W2902016849 @default.
- W3211639065 cites W2905412754 @default.
- W3211639065 cites W2910301662 @default.
- W3211639065 cites W2912651793 @default.
- W3211639065 cites W2913782058 @default.
- W3211639065 cites W2914466649 @default.
- W3211639065 cites W2915295488 @default.
- W3211639065 cites W2917157151 @default.
- W3211639065 cites W2917534044 @default.
- W3211639065 cites W2919054863 @default.
- W3211639065 cites W2919979744 @default.
- W3211639065 cites W2921575212 @default.
- W3211639065 cites W2923597310 @default.
- W3211639065 cites W2924958573 @default.
- W3211639065 cites W2926960342 @default.
- W3211639065 cites W2927975789 @default.
- W3211639065 cites W2940674768 @default.
- W3211639065 cites W2942406509 @default.
- W3211639065 cites W2946361138 @default.
- W3211639065 cites W2947678382 @default.
- W3211639065 cites W2951604549 @default.
- W3211639065 cites W2957685090 @default.
- W3211639065 cites W2960187325 @default.
- W3211639065 cites W2962004477 @default.
- W3211639065 cites W2964498586 @default.
- W3211639065 cites W2969323609 @default.
- W3211639065 cites W2969747083 @default.
- W3211639065 cites W2972062673 @default.
- W3211639065 cites W2980886541 @default.
- W3211639065 cites W2984259954 @default.
- W3211639065 cites W2990991805 @default.
- W3211639065 cites W2991340219 @default.
- W3211639065 cites W3000166736 @default.
- W3211639065 cites W3003416411 @default.
- W3211639065 cites W3005880646 @default.
- W3211639065 cites W3007156934 @default.
- W3211639065 cites W3009134570 @default.
- W3211639065 cites W3010891479 @default.
- W3211639065 cites W3011794532 @default.