Multi-threshold image segmentation using new strategies enhanced whale optimization for lupus nephritis pathological images☆

doi:10.1016/j.displa.2024.102799

科研成果详情

题名	Multi-threshold image segmentation using new strategies enhanced whale optimization for lupus nephritis pathological images☆
作者	Shi, Jinge 1; Chen, Yi 1; Wang, Chaofan 1; Heidari, Ali Asghar 2; Liu, Lei 3; Chen, Huiling 1; Chen, Xiaowei 4; Sun, Li4
发表日期	2024-09
发表期刊	Displays 影响因子和分区
语种	英语
原始文献类型	Article
关键词	Whale optimization algorithm Magnetic liquid climbing Image segmentation Topological mapping Non-local means 2D histogram
其他关键词	PARTICLE SWARM OPTIMIZATION ; GLOBAL OPTIMIZATION ; DIFFERENTIAL EVOLUTION ; INSPIRED OPTIMIZER ; ALGORITHM ; INTELLIGENCE ; NETWORK ; DESIGN ; TESTS
摘要	Lupus Nephritis (LN) has been considered as the most prevalent form of systemic lupus erythematosus. Medical imaging plays an important role in diagnosing and treating LN, which can help doctors accurately assess the extent and extent of the lesion. However, relying solely on visual observation and judgment can introduce subjectivity and errors, especially for complex pathological images. Image segmentation techniques are used to differentiate various tissues and structures in medical images to assist doctors in diagnosis. Multi-threshold Image Segmentation (MIS) has gained widespread recognition for its direct and practical application. However, existing MIS methods still have some issues. Therefore, this study combines non-local means, 2D histogram, and 2D Renyi's entropy to improve the performance of MIS methods. Additionally, this study introduces an improved variant of the Whale Optimization Algorithm (GTMWOA) to optimize the aforementioned MIS methods and reduce algorithm complexity. The GTMWOA fusions Gaussian Exploration (GE), Topology Mapping (TM), and Magnetic Liquid Climbing (MLC). The GE effectively amplifies the algorithm's proficiency in local exploration and quickens the convergence rate. The TM facilitates the algorithm in escaping local optima, while the MLC mechanism emulates the physical phenomenon of MLC, refining the algorithm's convergence precision. This study conducted an extensive series of tests using the IEEE CEC 2017 benchmark functions to demonstrate the superior performance of GTMWOA in addressing intricate optimization problems. Furthermore, this study executed an experiment using Berkeley images and LN images to verify the superiority of GTMWOA in MIS. The ultimate outcomes of the MIS experiments substantiate the algorithm's advanced capabilities and robustness in handling complex optimization problems.
资助项目	Natural Science Foundation of Zhejiang Province [LZ22F020005]; National Natural Science Foundation of China [62076185, 62301367]
出版者	ELSEVIER
ISSN	0141-9382
EISSN	1872-7387
卷号	84
DOI	10.1016/j.displa.2024.102799
页数	42
WOS类目	Computer Science, Hardware & Architecture ; Engineering, Electrical & Electronic ; Instruments & Instrumentation ; Optics
WOS研究方向	Computer Science ; Engineering ; Instruments & Instrumentation ; Optics
WOS记录号	WOS:001295592800001
收录类别	SCIE ; SCOPUS ; EI
EI入藏号	20243316877346
EI主题词	Image segmentation
EI分类号	405.3 Surveying ; 461.1 Biomedical Engineering ; 461.6 Medicine and Pharmacology ; 721.1 Computer Theory, Includes Formal Logic, Automata Theory, Switching Theory, Programming Theory ; 746 Imaging Techniques ; 921.4 Combinatorial Mathematics, Includes Graph Theory, Set Theory ; 921.5 Optimization Techniques
URL	查看原文
SCOPUSEID	2-s2.0-85200950722
通讯作者地址	[Chen, Huiling]Wenzhou Univ, Key Lab Intelligent Informat Safety & Emergency Zh, Wenzhou 325035, Peoples R China. ; [Chen, Xiaowei;Sun, Li]Wenzhou Med Univ, Affiliated Hosp 1, Dept Rheumatol & Immunol, Wenzhou 325000, Peoples R China.
Scopus学科分类	Human-Computer Interaction;Hardware and Architecture;Electrical and Electronic Engineering
SCOPUS_ID	SCOPUS_ID:85200950722
引用统计
文献类型	期刊论文
条目标识符	https://kms.wmu.edu.cn/handle/3ETUA0LF/217357
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; Sun, Li
作者单位	1.Wenzhou Univ, Key Lab Intelligent Informat Safety & Emergency Zh, Wenzhou 325035, 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	Shi, Jinge,Chen, Yi,Wang, Chaofan,et al. Multi-threshold image segmentation using new strategies enhanced whale optimization for lupus nephritis pathological images☆[J]. Displays,2024,84.
APA	Shi, Jinge., Chen, Yi., Wang, Chaofan., Heidari, Ali Asghar., Liu, Lei., ... & Sun, Li. (2024). Multi-threshold image segmentation using new strategies enhanced whale optimization for lupus nephritis pathological images☆. Displays, 84.
MLA	Shi, Jinge,et al."Multi-threshold image segmentation using new strategies enhanced whale optimization for lupus nephritis pathological images☆".Displays 84(2024).