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Performance optimization of salp swarm algorithm for multi-threshold image segmentation: Comprehensive study of breast cancer microscopy

Computers in biology and medicine, 2021-12, Vol.139, p.105015-105015, Article 105015 [Peer Reviewed Journal]

2021 Elsevier Ltd ;Copyright © 2021 Elsevier Ltd. All rights reserved. ;2021. Elsevier Ltd ;ISSN: 0010-4825 ;EISSN: 1879-0534 ;DOI: 10.1016/j.compbiomed.2021.105015 ;PMID: 34800808

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Title:
Performance optimization of salp swarm algorithm for multi-threshold image segmentation: Comprehensive study of breast cancer microscopy
Author: Zhao, Songwei ; Wang, Pengjun ; Heidari, Ali Asghar ; Chen, Huiling ; He, Wenming ; Xu, Suling
Subjects: Accuracy ; Algorithms ; Breast cancer ; Breast Neoplasms - diagnostic imaging ; Engineering ; Fault diagnosis ; Feature selection ; Female ; Genetic algorithms ; Humans ; Image processing ; Image Processing, Computer-Assisted ; Image segmentation ; Kapur's entropy ; Medical imaging ; Medical research ; Meta-heuristic algorithms ; Methods ; Microscopy ; Multi-threshold image segmentation ; Mutation ; Neural networks ; Object recognition ; Optimization ; Optimization algorithms ; Organs ; Performance optimization ; Power plants ; Qualitative analysis ; Rehabilitation ; Salp swarm algorithm ; Segmentation ; Tissue analysis ; Traveling salesman problem
Is Part Of: Computers in biology and medicine, 2021-12, Vol.139, p.105015-105015, Article 105015
Description: 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. •An improved Salp Swarm Algorithm (EHSSA) is proposed.•An effective position updating mechanism is integrated into SSA.•The better performance of EHSSA is verified over a set of algorithms.•EHSSA is tested on CEC2014 functions and image segmentation problems.
Publisher: United States: Elsevier Ltd
Language: English
Identifier: ISSN: 0010-4825
EISSN: 1879-0534
DOI: 10.1016/j.compbiomed.2021.105015
PMID: 34800808
Source: MEDLINE
ProQuest Central

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Performance optimization of salp swarm algorithm for multi-threshold image segmentation: Comprehensive study of breast cancer microscopy

2021 Elsevier Ltd ;Copyright © 2021 Elsevier Ltd. All rights reserved. ;2021. Elsevier Ltd ;ISSN: 0010-4825 ;EISSN: 1879-0534 ;DOI: 10.1016/j.compbiomed.2021.105015 ;PMID: 34800808

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