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A multi-threshold image segmentation method based on arithmetic optimization algorithm: A real case with skin cancer dermoscopic images

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2025年第2期12卷 112-137页

作者： Hao, Shuhui Huang, Changcheng Chen, Yi Wang, Mingjing Liu, Lei Xu, Suling Chen, Huiling Wenzhou Univ Key Lab Intelligent Informat Safety & Emergency Zh Wenzhou 325035 Zhejiang Peoples R China Wenzhou Univ Technol Sch Data Sci & Artificial Intelligence Wenzhou 325000 Zhejiang Peoples R China Sichuan Univ Coll Comp Sci Chengdu 610065 Sichuan Peoples R China Ningbo Univ Affiliated Hosp Med Sch Dept Dermatol Ningbo 315020 Zhejiang Peoples R China

multi-threshold image segmentation (MTIS) is a crucial technology in image processing, characterized by simplicity and efficiency, and the key lies in the selection of thresholds. However, the method's time complexity will grow exponentially with the number of thresholds. To solve this problem, an improved arithmetic optimization algorithm (ETAOA) is proposed in this paper, an optimizer for optimizing the process of merging appropriate thresholds. Specifically, two optimization strategies are introduced to optimize the optimal threshold process: elite evolutionary strategy (EES) and elite tracking strategy (ETS). First, to verify the optimization performance of ETAOA, mechanism comparison experiments, scalability tests, and comparison experiments with nine state-of-the-art peers are executed based on the benchmark functions of CEC2014 and CEC2022. After that, to demonstrate the feasibility of ETAOA in the segmentation domain, comparison experiments were performed using 10 advanced segmentation methods based on skin cancer dermatoscopy image datasets under low and high thresholds, respectively. The above experimental results show that the proposed ETAOA performs outstanding optimization compared with benchmark functions. Moreover, the experimental results in the segmentation domain show that ETAOA has superior segmentation performance under low and high threshold conditions.

关键词： meta-heuristic algorithms arithmetic optimization algorithm skin cancer multi-threshold image segmentation

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Dung beetle optimization with composite population initialization and multi-strategy learning for multi-level threshold image segmentation

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SIGNAL image AND VIDEO PROCESSING 2025年第3期19卷 1-15页

作者： Li, Zhidan Liu, Wei Zhao, Hongying Pu, Wenjing Southwest Petr Univ Sch Elect Engn & Informat Chengdu 610500 Peoples R China

As the number of thresholds increases in the multi-threshold segmentation of digital images, the complexity of determining the ideal thresholds rises sharply, posing significant challenges for conventional approaches. Dung Beetle Optimization (DBO) is a metaheuristic algorithm that mimics the behaviors of dung beetles, including rolling dung balls, female beetles laying eggs, small beetles searching for food, and thief beetles stealing. However, the original DBO suffers from slow convergence rate and suboptimal solutions. This paper proposes an improved DBO algorithm, named DBO with composite population initialization and multi-strategy learning (CMDBO), to address the issues. The improvements include initializing the population using chaotic mapping and oppositional learning, enabling weaker individuals to learn from better ones, and applying quasi-center oppositional-based learning to enhance convergence rate and solution accuracy. To verify its search performance, CMDBO was tested on CEC2017 function set and compared with several algorithms. Furthermore, CMDBO was applied to multi-threshold image segmentation. Experimental results indicate that the proposed CMDBO achieved the best overall performance in terms of convergence speed and solution accuracy.

关键词： multi-threshold image segmentation Dung beetle optimization Composite population Oppositional-based learning

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multi-threshold image segmentation based on an improved Salp Swarm Algorithm: Case study of breast cancer pathology images

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COMPUTERS IN BIOLOGY AND MEDICINE 2024年 168卷 107769-107769页

作者： Guo, Hongliang Li, Mingyang Liu, Hanbo Chen, Xiao Cheng, Zhiqiang Li, Xiaohua Yu, Helong He, Qiuxiang Jilin Agr Univ Coll Informat Technol Changchun 130118 Peoples R China Jilin Agr Univ Coll Resources & Environm Changchun 130118 Peoples R China Sci & Technol Innovat Ctr Hlth Prod & Med Mat Char Changchun 130000 Peoples R China Wenzhou Univ Library Wenzhou 325035 Zhejiang Peoples R China Wenzhou Med Univ Affiliated Hosp 1 Dept Pathol Wenzhou 325000 Peoples R China

Breast cancer poses a significant risk to women's health, and it is essential to provide proper diagnostic support. Medical image processing technology is a key component of all supporting diagnostic techniques, with image segmentation (IS) being one of its primary steps. Among various methods, multilevel image segmentation (MIS) is considered one of the most effective and straightforward approaches. Many researchers have attempted to improve the quality of image segmentation by combining different metaheuristic algorithms with MIS. However, these methods often suffer from issues such as low convergence accuracy and a proclivity for converging towards Local Optima (LO). To overcome these challenges, this study introduces an integrated approach that combines the Salp Swarm Algorithm (SSA), Slime Mould Algorithm (SMA) and Differential Evolution (DE) algorithm. In this manuscript, we introduce an innovative hybrid MIS model termed SDSSA, which leverages elements from the SSA, SMA and DE algorithms. The SDSSA model fundamentally relies on non-local means 2D histogram and 2D Kapur's entropy. To evaluate the proposed method effectively, we compare it initially with similar algorithms using the IEEE CEC2014 benchmark functions. The SDSSA showcases enhanced convergence velocity and precision relative to similar algorithms. Furthermore, this paper proposes an excellent MIS method. Subsequently, IS experiments were conducted separately at both low and high threshold levels. The test results demonstrate that the segmentation outcomes of MIS, at both low and high threshold levels, outperform other methods. This validates SDSSA as a superior segmentation technique that provides practical assistance for future research in breast cancer pathology image processing.

关键词： Salp swarm algorithm Slime mould algorithm Differential evolution multi-threshold image segmentation Kapur's entropy

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multi-threshold image segmentation research based on improved enhanced arithmetic optimization algorithm

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SIGNAL image AND VIDEO PROCESSING 2024年第5期18卷 4045-4058页

作者： Li, Hanyu Zhu, Xiaoliang Li, Mengkun Yang, Ziwei Wen, Mengke Xinjiang Univ Coll Software Urumqi 830008 Peoples R China Xinjiang Univ Xinjiang Uygur Autonomous Reg Big Data & Intellige Urumqi 830008 Peoples R China Xinjiang Med Univ Affiliated Tumor Hosp Urumqi 830011 Peoples R China

Aiming at the shortcomings of arithmetic optimization algorithm (AOA), which has low efficiency and is prone to fall into local optimal solutions, this paper proposes an improved AOA, called IAOA, and applies it to multi-threshold image segmentation processing. Compared with the original algorithm, the improvement points are: Firstly, the AOA introduces inertia weights and cosine factors to enhance the stability of the algorithm, then the chaotic mapping is embedded into the algorithm selection search stage to accelerate the convergence speed, and finally, the strategy of Cauchy mutation and the strategy of inverse learning are introduced to avoid the algorithm from falling into the local optimum. In this paper, 23 benchmark functions in CEC-2005 are used to test the performance of IAOA, and 8 commonly used image segmentation metrics were also selected to test the image segmentation effect of IAOA algorithm. In addition, several heuristic algorithms were also selected for comparison. The results show that compared with other algorithms, IAOA is more robust and has better segmentation performance.

关键词： Arithmetic optimization algorithm (AOA) Improved algorithm multi-threshold image segmentation Cauchy mutation Reverse learning

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multi-threshold image segmentation using a multi-strategy shuffled frog leaping algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期194卷 116511-116511页

作者： Chen, Yi Wang, Mingjing Heidari, Ali Asghar Shi, Beibei Hu, Zhongyi Zhang, Qian Chen, Huiling Mafarja, Majdi Turabieh, Hamza East China Normal Univ Sch Comp Sci & Technol Shanghai 200062 Peoples R China Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Zhejiang Peoples R China Jiangsu Univ Affiliated Peoples Hosp 8 Dianli Rd Zhenjiang 212000 Jiangsu Peoples R China Wenzhou Univ Technol Sch Data Sci & Artificial Intelligence Wenzhou 325035 Peoples R China Birzeit Univ Dept Comp Sci POB 14 West Bank Palestine Taif Univ Dept Informat Technol Coll Comp & Informat Technol POB 11099 At Taif 21944 Saudi Arabia

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 (BSD5500). 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.

关键词： Medical image segmentation multi-threshold image segmentation Shuffled frog leaping algorithm Horizontal and vertical crossover search Kapur's entropy image Evolutionary algorithms Optimization Swarm intelligence

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

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COMPUTERS IN BIOLOGY AND MEDICINE 2021年 139卷 105015-105015页

作者： Zhao, Songwei Wang, Pengjun Heidari, Ali Asghar Chen, Huiling He, Wenming Xu, Suling Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Zhejiang Peoples R China Wenzhou Univ Coll Elect & Elect Engn Wenzhou 325035 Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran Iran Ningbo Univ Affiliated Hosp Med Sch Ningbo 315020 Peoples R China

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://***.

关键词： Meta-heuristic algorithms Salp swarm algorithm multi-threshold image segmentation Kapur 's entropy Breast cancer Performance optimization

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An improved weighted mean of vectors optimizer for multi-threshold image segmentation: case study of breast cancer

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第10期27卷 13945-14004页

作者： Hao, Shuhui Huang, Changcheng Heidari, Ali Asghar Chen, Huiling Liang, Guoxi Wenzhou Univ Dept Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran Iran Wenzhou Univ Key Lab Intelligent Informat Safety & Emergency Zh Wenzhou 325035 Peoples R China Wenzhou Polytech Dept Artificial Intelligence Wenzhou 325035 Peoples R China

Women are commonly diagnosed with breast cancer (BC), and early detection can significantly increase the cure rate. This study suggested a multi-threshold image segmentation (MTIS) technique for dividing BC histological slice images to assist in identifying lesions and boost diagnostic effectiveness. The selection of the threshold combination, a challenging combinatorial optimization problem, is the key to the MTIS approach. To enhance the MTIS method, a variant of INFO (BQINFO) is proposed to optimize the threshold combination selection procedure. BQINFO is constructed by introducing the barebones mechanism (BM) and quasi-opposition-based learning (QOBL) to INFO and addressing its slow convergence and weakness in local stagnation. To evaluate the optimization performance of BQINFO and the positive impact influence of introducing QOBL and BM to the original INFO for the acceleration of convergence speed and the solution of local stagnation, a series of comparative experiments were carried out using CEC2014 and CEC2021. The comprehensive results and comparisons obtained from the optimization indicators indicate the outstanding performance of BQINFO in overcoming the slow convergence and local stagnation problems when dealing with benchmark function problems. Besides, to further validate BQINFO's performance optimization of threshold combination selection, this paper performed an MTIS experiment with R & eacute;nyi's entropy as the objective function on BSD500 images and BC histological slice images, respectively, providing qualitative and quantitative analysis with three evaluation metrics, FSIM, PSNR, and SSIM at low and high threshold levels. Ultimately, the experimental results demonstrate that BQINFO performs better and finds the optimal combination of thresholds faster than other comparison algorithms for both low and high threshold levels.

关键词： Weighted mean of vectors multi-threshold image segmentation Global optimization Barebones Quasi-opposition-based learning

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multi-threshold image segmentation based on Army ant optimizer 23

Multi-threshold image segmentation based on Army ant optimiz...

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Proceedings of the 2023 6th International Conference on Artificial Intelligence and Pattern Recognition

作者： Xuanyu Wang Dongming Xu School of Communications and Information Engineering & School of Artificial Intelligence Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9798400707674

Abstract: thresholding is a well-established method in the field of image segmentation and is widely used. The Otsu method is an algorithm that works well, but suffers from the disadvantages of time-consuming computational methods and insufficient progress when used for multi-threshold image segmentation. In this paper, we propose a multi-threshold segmentation algorithm based on the Army Ant Optimizer (AAO-OTSU), which is inspired by a novel population intelligence optimization algorithm: the Army Ant Optimizer (AAO). In this paper, we propose an OTSU multi-threshold image segmentation method (AAO-OTSU) based on marching ant optimization, which transforms the problem of finding the optimal threshold into an objective function optimization problem, i.e., finding the optimal multi-threshold based on the idea of maximum inter-class variance, using the inter-class variance as the fitness function in the iterative process, imitating the heuristic search of marching ant foraging behavior, and finding the optimal threshold of the image when the marching ant finds the optimal fitness, the position of the marching ant is the optimal threshold. This method transforms the problem of multi-threshold optimal solution into a multi-dimensional variable solving problem, which greatly saves the problem solving time and effectively optimizes the solving process. In order to verify the feasibility of the algorithm, classical standard images are used for threshold segmentation experiments while the convergence of the algorithm in this paper is compared with the classical metaheuristic algorithm for OTSU threshold segmentation. The results show that the method proposed in this paper can obtain better thresholds and effectively optimize the solution process, which not only improves the segmentation quality, but also the design of the double update operator ensures the correctness and faster convergence of the algorithm.

关键词： Army ant optimizer multi-threshold image segmentation OTSU method

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Efficient segmentation of Breast Cancer CT images: A multi-threshold image segmentation Method Based on Learning Search Algorithm 24

Efficient Segmentation of Breast Cancer CT Images: A Multi-T...

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1st International Conference on image Processing Machine Learning and Pattern Recognition

作者： Chen, Jun Wei, Qinshao Dong, Shanshan Qin, Chunlin Pan, Tingjiang Lu, Zenghui Qu, Chiwen Youjiang Med Univ Natl Sch Humanities & Management Baise Peoples R China Youjiang Med Univ Natl Sch Med Imaging Baise Peoples R China Youjiang Med Univ Natl Sch Languages & Cultures Baise Peoples R China Baise Hlth Meteorol Integrat Innovat Ctr Baise Peoples R China

ISBN: (纸本)9798400707032

Early diagnosis of breast cancer is crucial for improving patient survival rates, with medical imaging technology playing a key role. Traditional image segmentation methods suffer from limitations such as sensitivity to thresholds and a tendency to become trapped in local optima. Metaheuristic algorithms (MAs) offer an effective strategy to address these issues. This study introduces a novel optimization algorithm-Learning Search Algorithm (LSA)-that simulates human learning behaviors, including historical experience, social role modeling, and proactive learning, to optimize multi-threshold image segmentation. The LSA employs dynamic adaptive control parameters during the global search and local exploitation phases, enhancing segmentation accuracy and convergence speed. In the application of breast cancer CT image segmentation, the LSA outperforms existing advanced algorithms, providing higher quality solutions. This research not only enriches the repertoire of image segmentation algorithms but also provides new technical support for the early diagnosis and treatment of breast cancer.

关键词： Breast Cancer multi-threshold image segmentation Learning Search Algorithm

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An Improved Beluga Whale Optimization Algorithm by Collaborative Strategies for multi-threshold image segmentation 27

An Improved Beluga Whale Optimization Algorithm by Collabora...

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27th International Conference on Computer Supported Cooperative Work in Design (CSCWD)

作者： Liu, Mengran Xu, Hui Wu, Qinyue Dong, Chenbing Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China

ISBN: (纸本)9798350349184;9798350349191

multi-threshold segmentation techniques usually rely on exhaustive search, leading to an exponential growth in computational complexity with the increase in the number of thresholds. The combination of swarm intelligence and image segmentation is a promising approach to improve segmentation efficiency and reduce computational cost. Thus, this paper then proposes an Improved Beluga Whale Optimization (IBWO) algorithm incorporating collaborative strategies for multi-threshold image segmentation. As for the proposed IBWO algorithm, the following collaborative strategies are utilized. The logistic chaos mapping strategy can increase the randomness of the initial beluga population, the dynamic nonlinear equilibrium factor strategy can better balance the exploratory and exploitation phases, and the dynamic pinhole imaging strategy can increase the diversity of the population and prevent the population from falling into a local optimum. The objective function is minimum cross entropy, and experiments use benchmark images at different threshold levels to evaluate the method's performance and compare the IBWO algorithm with several other classical algorithms. The experimental results show that the proposed IBWO algorithm outperforms the comparative methods in all three image quality metrics, which are PSNR, SSIM, and FSIM.

关键词： multi-threshold image segmentation beluga whale optimization collaborative strategy

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