Stochastic biogeography-based learning improved RIME algorithm: application to image segmentation of lupus nephritis

doi:10.1007/s10586-024-04628-8

科研成果详情

题名	Stochastic biogeography-based learning improved RIME algorithm: application to image segmentation of lupus nephritis
作者	Zheng, Boli 1; Chen, Yi 1; Wang, Chaofan 1; Heidari, Ali Asghar 2; Liu, Lei 3; Chen, Huiling 1; Chen, Xiaowei 4; Chen, Peirong4
发表日期	2024-07-17
发表期刊	Cluster Computing 影响因子和分区
语种	英语
原始文献类型	Article ; Early Access
关键词	Swarm intelligence algorithm Chaotic mapping Rime optimization algorithm Renal pathology Biogeography-based strategy
其他关键词	PARTICLE SWARM OPTIMIZATION ; GLOBAL OPTIMIZATION ; COMPUTATIONAL INTELLIGENCE ; DIFFERENTIAL EVOLUTION ; PATHOLOGY ; SEARCH ; TESTS
摘要	Lupus nephritis (LN) is the most common symptom of systemic lupus erythematosus, emphasizing its importance in the field of medicine. The growing frequency of LN has increased the need for effective image segmentation algorithms. With the increasing prevalence of LN, the demand for efficient image segmentation techniques has grown. To enhance the efficiency of image segmentation of LN, many researchers employ a methodology that integrates multi-threshold image segmentation (MTIS) with metaheuristic algorithms (MAs). However, conventional MAs-based MTIS methods tend to converge towards local optima and have slow convergence rates, resulting in poor segmentation results within a limited iteration number. To address these challenges, this study proposes an advanced optimization algorithm termed Biogeography-based Learning Rime Optimization Algorithm (BLRIME) and integrates it with the MTIS approach for LN image segmentation. MTIS employs a non-local means 2D histogram to gather image information and uses 2D Renyi's entropy as the fitness function. BLRIME builds upon the foundation of the RIME algorithm, incorporating two significant strategies. Firstly, the introduction of piecewise chaotic mapping (PCM) ameliorates the quality of the initial solution provided by the algorithm. Secondly, a stochastic biogeography-based learning strategy (SBLS) prevents the RIME algorithm from falling into the local optimum early. SBLS is proposed by this study based on the biogeography-based learning strategy. In order to assess the efficacy of the BLRIME, this paper devises a series of experiments to compare it with similar algorithms presented at IEEE CEC 2017. Experimental studies have been conducted to provide empirical evidence demonstrating the superior rates of convergence and precision achieved by BLRIME. Subsequently, the BLRIME-based MTIS algorithm is employed to segment the LN images compared to other peer algorithms. Furthermore, the peak signal-to-noise ratio, feature similarity index, and structural similarity index are utilized as evaluation metrics to assess the image segmentation outcomes. The experimental results prove that BLRIME demonstrates superior global search capabilities, resulting in remarkable outcomes in the segmentation of LN images.
资助项目	Wenzhou Science and Technology Foundation [Y2020922]; National Natural Science Foundation of China [62301367, 62076185]; Natural Science Foundation of Zhejiang Province [LZ22F020005]
出版者	SPRINGER
ISSN	1386-7857
EISSN	1573-7543
卷号	27 期号:10 页码:14185-14229
DOI	10.1007/s10586-024-04628-8
页数	45
WOS类目	Computer Science, Information Systems ; Computer Science, Theory & Methods
WOS研究方向	Computer Science
WOS记录号	WOS:001270467800001
收录类别	SCIE ; SCOPUS ; EI
EI入藏号	20242916727688
EI主题词	Image segmentation
EI分类号	405.3 Surveying ; 454.3 Ecology and Ecosystems ; 716.1 Information Theory and Signal Processing ; 723.1 Computer Programming ; 723.4 Artificial Intelligence ; 723.4.2 Machine Learning ; 731.1 Control Systems ; 921.5 Optimization Techniques ; 921.6 Numerical Methods ; 961 Systems Science
URL	查看原文
SCOPUSEID	2-s2.0-85198852111
通讯作者地址	[Chen, Huiling]Wenzhou Univ, Inst Big Data & Informat Technol, Wenzhou 325000, Peoples R China. ; [Chen, Xiaowei;Chen, Peirong]Wenzhou Med Univ, Affiliated Hosp 1, Dept Rheumatol & Immunol, Wenzhou 325000, Peoples R China.
Scopus学科分类	Software;Computer Networks and Communications
SCOPUS_ID	SCOPUS_ID:85198852111
引用统计
文献类型	期刊论文
条目标识符	https://kms.wmu.edu.cn/handle/3ETUA0LF/216716
专题	附属第一医院附属第一医院_风湿免疫科
通讯作者	Chen, Huiling; Chen, Xiaowei; Chen, Peirong
作者单位	1.Wenzhou Univ, Inst Big Data & Informat Technol, Wenzhou 325000, Peoples R China; 2.Univ Tehran, Coll Engn, Sch Surveying & Geospatial Engn, Tehran, Iran; 3.Sichuan Univ, Coll Comp Sci, Chengdu 610065, Sichuan, Peoples R China; 4.Wenzhou Med Univ, Affiliated Hosp 1, Dept Rheumatol & Immunol, Wenzhou 325000, Peoples R China
通讯作者单位	附属第一医院; 风湿免疫科
推荐引用方式 GB/T 7714	Zheng, Boli,Chen, Yi,Wang, Chaofan,et al. Stochastic biogeography-based learning improved RIME algorithm: application to image segmentation of lupus nephritis[J]. Cluster Computing,2024,27(10):14185-14229.
APA	Zheng, Boli., Chen, Yi., Wang, Chaofan., Heidari, Ali Asghar., Liu, Lei., ... & Chen, Peirong. (2024). Stochastic biogeography-based learning improved RIME algorithm: application to image segmentation of lupus nephritis. Cluster Computing, 27(10), 14185-14229.
MLA	Zheng, Boli,et al."Stochastic biogeography-based learning improved RIME algorithm: application to image segmentation of lupus nephritis".Cluster Computing 27.10(2024):14185-14229.