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An APO Algorithm Based on Taguchi Methods and Its Application in Multi-Level Image Segmentation 认领 引用
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作者 Jeng-Shyang Pan Yan-Na Wei +3 位作者 Ling-Da Chi Shu-Chuan Chu Ru-Yu Wang Junzo Watada 《Computers, Materials & Continua》 SCIE EI 2026年第5期814-837,共24页
Multilevel image segmentation is a critical task in image analysis,which imposes high requirements on the global search capability and convergence efficiency of segmentation algorithms.In this paper,an improved Artifi... Multilevel image segmentation is a critical task in image analysis,which imposes high requirements on the global search capability and convergence efficiency of segmentation algorithms.In this paper,an improved Artificial Protozoa Optimization algorithm,termed the two-stage Taguchi-assisted Gaussian–Levy Artificial Protozoa Optimization(TGAPO)algorithm,is proposed and applied tomultilevel image segmentation.The proposed algorithm adopts a two-stage evolutionary mechanism.In the first stage,Gaussian perturbation is introduced to enhance local search capability;in the second stage,Levy flight is incorporated to expand the global search range;and finally,the Taguchi strategy is employed to further refine the optimal solution.Consequently,the global optimization performance and robustness of the algorithm are significantly improved.To evaluate the effectiveness of the proposed TGAPO algorithm,comparative experiments are conducted with representative optimization algorithms,including the Grey Wolf Optimizer(GWO)and Particle Swarm Optimization(PSO),in the context ofmultilevel image segmentation.The segmentation quality is assessed using the minimum cross-entropy function as the performance metric.Experimental results demonstrate that the TGAPO algorithm outperforms the comparison algorithms in terms of segmentation accuracy and convergence speed,and exhibits superior stability in high-threshold segmentation tasks.Furthermore,the proposedmethod achieves excellentmulti-threshold segmentation performance for color images and shows strong potential for practical applications. 展开更多
关键词 Meta-heuristic algorithm multilevel image segmentation taguchi strategy minimum cross-entropy threshold artificial protozoa optimization(APO)
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AGPLO-Driven Optimisation for Accurate Segmentation of Papillary Thyroid Carcinoma in Medical Imaging 认领 引用
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作者 Jing Ruan Xiaoxiao Chen +12 位作者 Hanbing Yao Yujia Xu Shiqi Xu Shihao Zhao Yulun Wu Yingting Dai Yubing Chen Shuqing Ma Qiongying Zhang Ying Zhou Ali Asghar Heidari Huiling Chen Yangping Shentu 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第3期847-858,共12页
Papillary Thyroid Carcinoma(PTC)is the most prevalent thyroid malignancy,and accurate lesion segmentation is essential for clinical diagnosis and treatment planning.Metaheuristic optimisation algorithms have been wide... Papillary Thyroid Carcinoma(PTC)is the most prevalent thyroid malignancy,and accurate lesion segmentation is essential for clinical diagnosis and treatment planning.Metaheuristic optimisation algorithms have been widely used in Multi-Threshold Image Segmentation(MTIS),but many existing methods suffer from an imbalance between global exploration and local exploitation.This study aims to develop a robust and well-balanced optimisation algorithm to improve the accuracy and stability of MTIS for PTC images.An Adaptive Guided Polar Lights Optimisation(AGPLO)algorithm is proposed,which incorporates an adaptive phase-shift operator,magnetic guiding convergence,and energy burst exploration mechanisms to dynamically regulate search behaviour.AGPLO was evaluated on the IEEE CEC2017 benchmark suite and applied to Rényi entropy-based MTIS for PTC image segmentation.Experimental results on benchmark functions demonstrate that AGPLO outperforms several original and advanced metaheuristic algorithms in terms of convergence accuracy,stability,and robustness.In PTC image segmentation experiments,AGPLO achieves superior PSNR,SSIM,and FSIM values,producing clearer lesion boundaries and preserving structural details more effectively than comparative methods.The proposed AGPLO provides an effective and reliable optimisation framework for MTIS and shows strong potential for intelligent medical image analysis applications. 展开更多
关键词 AGPLO medical image segmentation metaheuristic algorithms papillary thyroid carcinoma
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Hybrid Ensemble and Federated Learning Framework for Privacy-Preserving Cardiovascular MRI Segmentation 认领 引用
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作者 Karim Gasmi Afrah Alanazi +4 位作者 Inam Alanazi Sahar Almenwer Norah Alanazi Sarah Almaghrabi Samia Yahyaoui 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第6期1245-1287,共43页
Cardiac magnetic resonance imaging(MRI)segmentation is an essential aspect of quantitative cardiovascular analysis,facilitating accurate evaluation of ventricular volumes,myocardial mass,and functional parameters.Deep... Cardiac magnetic resonance imaging(MRI)segmentation is an essential aspect of quantitative cardiovascular analysis,facilitating accurate evaluation of ventricular volumes,myocardial mass,and functional parameters.Deep learning-based segmentation models have shown strong performance on benchmark datasets such as ACDC,but they remain challenging to deploy in real-world multi-centre settings.Data privacy laws make it hard to share data across institutions,and differences in imaging protocols and patient populationsmean that data is not always distributed in the same way(non-IID).This can have a big impact on how wellmodels work together and how well they generalise.To address these issues,we first evaluate advanced segmentation architectures,including UNet++and FPN with EfficientNet-based encoders,and assess multiple hybrid combinations at the probability level.We further improve the ensemble strategy by using a genetic algorithm to automatically identify the optimal model-weighting scheme,rather than fixed combination coefficients.The genetic algorithm explores the solution space to identify the optimal weight configuration based on segmentation metrics.The best hybrid configuration is then chosen as the input architecture for the federated learning stage.We propose a privacy-preserving federated ensemble framework that enables multiple clients to collaboratively train segmentation models without sharing raw MRI data.We methodically evaluate three federated optimisation strategies:FedAvg under IID and non-IID client distributions,and FedProx,which incorporates proximal regularisation to reduce client drift.The genetically optimised ensemble is always used in all federated setups.A thorough analysis of ACDC testing volumes employing overlap-and boundary-based metrics illustrates that the amalgamation of hybrid learning with genetic optimisation and federated training enhances robustness in heterogeneous environments while maintaining data confidentiality,thus providing an efficient approach for secure multi-centre cardiac MRI segmentation. 展开更多
关键词 SDG 3 cardiovascular imaging segmentation ensemble deep learning genetic algorithm federated learning privacy-preserving AI
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A Control-based Transition Reinforced Optimization Process for Multi-level Threshold Image Segmentation 认领 引用
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作者 Wei Wang Peiying Zhang +5 位作者 Saleh Ali Alomari Raed Abu Zitar Aseel Smerat Mohamed Sharaf Absalom E.Ezugwu Laith Abualigah 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第2期1061-1087,共27页
In this study,we present a novel approach to multi-threshold image segmentation using an adaptive method that combines the Ebola Optimization Search Algorithm(EOSA)with the Aquila Optimizer,termed the Integrated Enhan... In this study,we present a novel approach to multi-threshold image segmentation using an adaptive method that combines the Ebola Optimization Search Algorithm(EOSA)with the Aquila Optimizer,termed the Integrated Enhanced Ebola Optimization Search Algorithm(IEOSA).Our approach leverages this integration to produce high-quality segmented images.The IEOSA method introduces two distinct optimization mechanisms to identify optimal solutions.By blending the randomness of the Aquila Optimizer with the capabilities of EOSA,we enhance the exploration potential of the algorithm.Additionally,we incorporate a self-transition learning system within the IEOSA to further boost its performance.To tackle multi-level threshold image segmentation,we apply Kapur’s entropy between-class variance within the IEOSA framework.Our findings show that the IEOSA-based techniques outperform other comparable methods,offering faster convergence and more stable segmentation results.Through comparative analysis using standard test images,we demonstrate that IEOSA achieves higher solution accuracy than other methods.Ultimately,the proposed IEOSA methodologies effectively address multi-level threshold image segmentation challenges,accurately segmenting even the minor errors that are often overlooked in high-resolution images. 展开更多
关键词 Ebola optimization search algorithm(EOSA) Aquila optimizer(AO) Multi-level threshold Image segmentation Transition mechanism
Enhanced Prostate Tumor Segmentation in MRI Using Hybrid Optimization and Adaptive Thresholding 认领 引用
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作者 Wafa Gtifa Ayoub Mhaouch Anis Sakly 《iRADIOLOGY》 CSCD 2026年第2期157-170,共14页
Background:Accurate segmentation of prostate tumors in magnetic resonance imaging(MRI)is critical for improving diagnostic accuracy and supporting clinical decision making.However,many existing approaches rely on supe... Background:Accurate segmentation of prostate tumors in magnetic resonance imaging(MRI)is critical for improving diagnostic accuracy and supporting clinical decision making.However,many existing approaches rely on supervised learning methods that require large annotated datasets and substantial computational resources,limiting their clinical applicability.This study aims to develop and evaluate a fully unsupervised framework for prostate tumor segmentation in multiparametric MRI using hybrid optimization and adaptive thresholding techniques.Methods:This study proposes an unsupervised prostate tumor segmentation framework based on hybrid optimization and adaptive thresholding.Two metaheuristic optimization algorithms,chaotic particle swarm optimization and forest optimization,were employed to optimize Otsu's variance-based thresholding and Kapur's entropy-based thresholding,resulting in four hybrid configurations.The framework was evaluated using multiparametric prostate MRI datasets,including apparent diffusion coefficient,T2-weighted,and diffusion-weighted imaging sequences.Segmentation performance was assessed using overlapbased and classification-based metrics.Statistical analysis included the computation of descriptive performance measures and confidence intervals to evaluate robustness and consistency across datasets.Results:The proposed framework demonstrated reliable and consistent segmentation performance across all MRI modalities.The Otsu-based hybrid configurations showed superior overlap and classification performance in diffusion-based imaging,whereas the entropy-based methods exhibited more conservative behavior on heterogeneous T2-weighted images.Overall,the optimization-based approaches achieved high segmentation accuracy and stability without the need for annotated training data.Conclusions:The proposed hybrid optimization and thresholding framework provides an effective,fully unsupervised solution for prostate tumor segmentation in multiparametric MRI.Its robustness,computational efficiency,and independence from training data highlight its potential for integration into clinical prostate cancer diagnostic workflows. 展开更多
关键词 adaptive thresholding magnetic resonance imaging optimization algorithms prostate cancer tumor segmentation
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Comparative evaluation of threshold-based and CNN-based segmentation methods for multi-modal digital images of geotechnical materials 认领 引用
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作者 Zhijie Jian Jiangfeng Liu +4 位作者 Shijia Ma Zhipeng Wang Qing Jian Ruinian Sun Chenghao Wu 《Intelligent Geoengineering》 2026年第1期11-35,共25页
This study systematically evaluates the performance of 15 conventional global single-threshold segmentation algorithms and three representative convolutional neural network(CNN)models across multi-modal geotechnical m... This study systematically evaluates the performance of 15 conventional global single-threshold segmentation algorithms and three representative convolutional neural network(CNN)models across multi-modal geotechnical material images,including computed tomography(CT)and scanning electron microscopy(SEM)data.Based on their characteristics,thresholding methods are classified into three categories:histogram-based,entropy-based,and other approaches.Four types of geotechnical material CT images and two types of SEM images were selected as the evaluation datasets,and an objective assessment criterion that does not require manual annotation was proposed.The results indicate that the performance of different thresholding algorithms varies considerably across imaging modalities:the Otsu method performs best on coal and sandstone CT images,the Liu-S method(implemented in the JHNY-DPM software)excels on sandy soil CT images,and the Yen method demonstrates strong robustness on SEM images.However,all thresholding methods fail to effectively segment granite images with uneven grayscale distributions.In contrast,deep learning models exhibit superior performance across all modalities,with U-Net achieving the highest accuracy and stability during both training and validation without noticeable overfitting,significantly outperforming Fcn and Deeplabv3.Further experiments on a combined CT-SEM dataset reveal that despite domain adaptation challenges,U-Net can consistently segment complex geotechnical structures across different imaging modalities.Overall,the analysis demonstrates that deep learning models substantially enhance the accuracy and robustness of multi-modal geotechnical image segmentation,providing guidance for algorithm selection and supporting the unified processing of multi-source imaging data toward automation and intelligent analysis in digital geotechnical research. 展开更多
关键词 Multi-modal image segmentation Thresholding algorithms Geotechnical materials Digital rock images Deep learning JHNY-DPM
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Improved SE-UNet network-based semantic segmentation and extraction of hidden geological significance in geological maps 认领 引用 被引量：1
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作者 Kai Ma Jun-jie Liu +5 位作者 Si-qi Lu Ze-hua Huang Miao Tian Jun-yuan Deng Zhong Xie Qin-jun Qiu 《China Geology》 CAS CSCD 2025年第4期643-660,共18页
Automatic segmentation and recognition of content and element information in color geological map are of great significance for researchers to analyze the distribution of mineral resources and predict disaster informa... Automatic segmentation and recognition of content and element information in color geological map are of great significance for researchers to analyze the distribution of mineral resources and predict disaster information.This article focuses on color planar raster geological map(geological maps include planar geological maps,columnar maps,and profiles).While existing deep learning approaches are often used to segment general images,their performance is limited due to complex elements,diverse regional features,and complicated backgrounds for color geological map in the domain of geoscience.To address the issue,a color geological map segmentation model is proposed that combines the Felz clustering algorithm and an improved SE-UNet deep learning network(named GeoMSeg).Firstly,a symmetrical encoder-decoder structure backbone network based on UNet is constructed,and the channel attention mechanism SENet has been incorporated to augment the network’s capacity for feature representation,enabling the model to purposefully extract map information.The SE-UNet network is employed for feature extraction from the geological map and obtain coarse segmentation results.Secondly,the Felz clustering algorithm is used for super pixel pre-segmentation of geological maps.The coarse segmentation results are refined and modified based on the super pixel pre-segmentation results to obtain the final segmentation results.This study applies GeoMSeg to the constructed dataset,and the experimental results show that the algorithm proposed in this paper has superior performance compared to other mainstream map segmentation models,with an accuracy of 91.89%and a MIoU of 71.91%. 展开更多
关键词 Geological map UNet model Image segmentation Semantic segmentation Pixel pre-segmentation Clustering algorithm Attention mechanism Deep learning Artificial intelligence Geological survey engineering
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Improved Reptile Search Algorithm by Salp Swarm Algorithm for Medical Image Segmentation 认领 引用 被引量：7
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作者 Laith Abualigah Mahmoud Habash +4 位作者 Essam Said Hanandeh Ahmad MohdAziz Hussein Mohammad Al Shinwan Raed Abu Zitar Heming Jia 《Journal of Bionic Engineering》 SCIE EI CSCD 2023年第4期1766-1790,共25页
This study proposes a novel nature-inspired meta-heuristic optimizer based on the Reptile Search Algorithm combed with Salp Swarm Algorithm for image segmentation using gray-scale multi-level thresholding,called RSA-S... This study proposes a novel nature-inspired meta-heuristic optimizer based on the Reptile Search Algorithm combed with Salp Swarm Algorithm for image segmentation using gray-scale multi-level thresholding,called RSA-SSA.The proposed method introduces a better search space to find the optimal solution at each iteration.However,we proposed RSA-SSA to avoid the searching problem in the same area and determine the optimal multi-level thresholds.The obtained solutions by the proposed method are represented using the image histogram.The proposed RSA-SSA employed Otsu’s variance class function to get the best threshold values at each level.The performance measure for the proposed method is valid by detecting fitness function,structural similarity index,peak signal-to-noise ratio,and Friedman ranking test.Several benchmark images of COVID-19 validate the performance of the proposed RSA-SSA.The results showed that the proposed RSA-SSA outperformed other metaheuristics optimization algorithms published in the literature. 展开更多
关键词 Bioinspired Reptile Search Algorithm Salp Swarm Algorithm Multi-level thresholding Image segmentation Meta-heuristic algorithm
Medical Image Segmentation using PCNN based on Multi-feature Grey Wolf Optimizer Bionic Algorithm 认领 引用 被引量：9
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作者 Xue Wang Zhanshan Li +2 位作者 Heng Kang Yongping Huang Di Gai 《Journal of Bionic Engineering》 SCIE EI CSCD 2021年第3期711-720,共10页
Medical image segmentation is a challenging task especially in multimodality medical image analysis.In this paper,an improved pulse coupled neural network based on multiple hybrid features grey wolf optimizer(MFGWO-PC... Medical image segmentation is a challenging task especially in multimodality medical image analysis.In this paper,an improved pulse coupled neural network based on multiple hybrid features grey wolf optimizer(MFGWO-PCNN)is proposed for multimodality medical image segmentation.Specifically,a two-stage medical image segmentation method based on bionic algorithm is presented,including image fusion and image segmentation.The image fusion stage fuses rich information from different modalities by utilizing a multimodality medical image fusion model based on maximum energy region.In the stage of image segmentation,an improved PCNN model based on MFGWO is proposed,which can adaptively set the parameters of PCNN according to the features of the image.Two modalities of FLAIR and TIC brain MRIs are applied to verify the effectiveness of the proposed MFGWO-PCNN algorithm.The experimental results demonstrate that the proposed method outperforms the other seven algorithms in subjective vision and objective evaluation indicators. 展开更多
关键词 grey wolf optimizer pulse coupled neural network bionic algorithm medical image segmentation
Segmentation algorithm of complex ore images based on templates transformation and reconstruction 认领 引用 被引量：7
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作者 Guo-ying Zhang Guan-zhou Liu Hong Zhu 《International Journal of Minerals,Metallurgy and Materials》 SCIE EI CAS CSCD 2011年第4期385-389,共5页
Lots of noises and heterogeneous objects with various sizes coexist in a complex image,such as an ore image;the classical image thresholding method cannot effectively distinguish between ores.To segment ore objects wi... Lots of noises and heterogeneous objects with various sizes coexist in a complex image,such as an ore image;the classical image thresholding method cannot effectively distinguish between ores.To segment ore objects with various sizes simultaneously,two adaptive windows in the image were chosen for each pixel;the gray value of windows was calculated by Otsu's threshold method.To extract the object skeleton,the definition principle of distance transformation templates was proposed.The ores linked together in a binary image were separated by distance transformation and gray reconstruction.The seed region of each object was picked up from the local maximum gray region of the reconstruction image.Starting from these seed regions,the watershed method was used to segment ore object effectively.The proposed algorithm marks and segments most objects from complex images precisely. 展开更多
关键词 ores image analysis image segmentation morphological transformation algorithms
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An Improved Jellyfish Algorithm for Multilevel Thresholding of Magnetic Resonance Brain Image Segmentations 认领 引用 被引量：6
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作者 Mohamed Abdel-Basset Reda Mohamed +3 位作者 Mohamed Abouhawwash Ripon K.Chakrabortty Michael J.Ryan Yunyoung Nam 《Computers, Materials & Continua》 SCIE EI 2021年第9期2961-2977,共17页
Image segmentation is vital when analyzing medical images,especially magnetic resonance(MR)images of the brain.Recently,several image segmentation techniques based on multilevel thresholding have been proposed for med... Image segmentation is vital when analyzing medical images,especially magnetic resonance(MR)images of the brain.Recently,several image segmentation techniques based on multilevel thresholding have been proposed for medical image segmentation;however,the algorithms become trapped in local minima and have low convergence speeds,particularly as the number of threshold levels increases.Consequently,in this paper,we develop a new multilevel thresholding image segmentation technique based on the jellyfish search algorithm(JSA)(an optimizer).We modify the JSA to prevent descents into local minima,and we accelerate convergence toward optimal solutions.The improvement is achieved by applying two novel strategies:Rankingbased updating and an adaptive method.Ranking-based updating is used to replace undesirable solutions with other solutions generated by a novel updating scheme that improves the qualities of the removed solutions.We develop a new adaptive strategy to exploit the ability of the JSA to find a best-so-far solution;we allow a small amount of exploration to avoid descents into local minima.The two strategies are integrated with the JSA to produce an improved JSA(IJSA)that optimally thresholds brain MR images.To compare the performances of the IJSA and JSA,seven brain MR images were segmented at threshold levels of 3,4,5,6,7,8,10,15,20,25,and 30.IJSA was compared with several other recent image segmentation algorithms,including the improved and standard marine predator algorithms,the modified salp and standard salp swarm algorithms,the equilibrium optimizer,and the standard JSA in terms of fitness,the Structured Similarity Index Metric(SSIM),the peak signal-to-noise ratio(PSNR),the standard deviation(SD),and the Features Similarity Index Metric(FSIM).The experimental outcomes and the Wilcoxon rank-sum test demonstrate the superiority of the proposed algorithm in terms of the FSIM,the PSNR,the objective values,and the SD;in terms of the SSIM,IJSA was competitive with the others. 展开更多
关键词 Magnetic resonance imaging brain image segmentation artificial jellyfish search algorithm ranking method local minima Otsu method
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Segmentation of Brain Tumor Magnetic Resonance Images Using a Teaching-Learning Optimization Algorithm 认领 引用 被引量：1
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作者 J.Jayanthi M.Kavitha +4 位作者 T.Jayasankar A.Sagai Francis Britto N.B.Prakash Mohamed Yacin Sikkandar C.Bharathiraja 《Computers, Materials & Continua》 SCIE EI 2021年第9期4191-4203,共13页
Image recognition is considered to be the pre-eminent paradigm for the automatic detection of tumor diseases in this era.Among various cancers identified so far,glioma,a type of brain tumor,is one of the deadliest can... Image recognition is considered to be the pre-eminent paradigm for the automatic detection of tumor diseases in this era.Among various cancers identified so far,glioma,a type of brain tumor,is one of the deadliest cancers,and it remains challenging to the medicinal world.The only consoling factor is that the survival rate of the patient is increased by remarkable percentage with the early diagnosis of the disease.Early diagnosis is attempted to be accomplished with the changes observed in the images of suspected parts of the brain captured in specific interval of time.From the captured image,the affected part of the brain is analyzed using magnetic resonance imaging(MRI)technique.Existence of different modalities in the captured MRI image demands the best automated model for the easy identification of malignant cells.Number of image processing techniques are available for processing the images to identify the affected area.This study concentrates and proposes to improve early diagnosis of glioma using a preprocessing boosted teaching and learning optimization(P-BTLBO)algorithm that automatically segments a brain tumor in an given MRI image.Preprocessing involves contrast enhancement and skull stripping procedures through contrast limited adaptive histogram equalization technique.The traditional TLBO algorithm that works with the perspective of teacher and the student is here improved by using a boosting mechanism.The results obtained using this P-BTLBO algorithm is compared on different benchmark images for the validation of its standard.The experimental findings show that P-BTLBO algorithm approach outperforms other existing algorithms of its kind. 展开更多
关键词 Brain tumor TLBO algorithm skull stripping preprocessing segmentation
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Foreign Fiber Image Segmentation Based on Maximum Entropy and Genetic Algorithm 认领 引用 被引量：4
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作者 Liping Chen Xiangyang Chen +2 位作者 Sile Wang Wenzhu Yang Sukui Lu 《Journal of Computer and Communications》 2015年第11期1-7,共7页
In machine-vision-based systems for detecting foreign fibers, due to the background of the cotton layer has the absolute advantage in the whole image, while the foreign fiber only account for a very small part, and w... In machine-vision-based systems for detecting foreign fibers, due to the background of the cotton layer has the absolute advantage in the whole image, while the foreign fiber only account for a very small part, and what’s more, the brightness and contrast of the image are all poor. Using the traditional image segmentation method, the segmentation results are very poor. By adopting the maximum entropy and genetic algorithm, the maximum entropy function was used as the fitness function of genetic algorithm. Through continuous optimization, the optimal segmentation threshold is determined. Experimental results prove that the image segmentation of this paper not only fast and accurate, but also has strong adaptability. 展开更多
关键词 Foreign Fibers Image Segmentation Maximum Entropy Genetic Algorithm
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Terrain Rendering LOD Algorithm Based on Improved Restrictive Quadtree Segmentation and Variation Coefficient of Elevation 认领 引用 被引量：4
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作者 Zhenwu Wang Xiaohua Lu 《Journal of Beijing Institute of Technology》 EI CAS 2018年第4期617-622,共6页
Aiming to deal with the difficult issues of terrain data model simplification and crack disposal,the paper proposed an improved level of detail(LOD)terrain rendering algorithm,in which a variation coefficient of eleva... Aiming to deal with the difficult issues of terrain data model simplification and crack disposal,the paper proposed an improved level of detail(LOD)terrain rendering algorithm,in which a variation coefficient of elevation is introduced to express the undulation of topography.Then the coefficient is used to construct a node evaluation function in the terrain data model simplification step.Furthermore,an edge reduction strategy is combined with the improved restrictive quadtree segmentation to handle the crack problem.The experiment results demonstrated that the proposed method can reduce the amount of rendering triangles and enhance the rendering speed on the premise of ensuring the rendering effect compared with a traditional LOD algorithm. 展开更多
关键词 terrain data model simplification crack disposal level of detail(LOD)terrain rendering algorithm variation coefficient of elevation node evaluation function restrictive quadtree segmentation
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Multi-Level Image Segmentation Combining Chaotic Initialized Chimp Optimization Algorithm and Cauchy Mutation 认领 引用 被引量：1
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作者 Shujing Li Zhangfei Li +2 位作者 Wenhui Cheng Chenyang Qi Linguo Li 《Computers, Materials & Continua》 SCIE EI 2024年第8期2049-2063,共15页
To enhance the diversity and distribution uniformity of initial population,as well as to avoid local extrema in the Chimp Optimization Algorithm(CHOA),this paper improves the CHOA based on chaos initialization and Cau... To enhance the diversity and distribution uniformity of initial population,as well as to avoid local extrema in the Chimp Optimization Algorithm(CHOA),this paper improves the CHOA based on chaos initialization and Cauchy mutation.First,Sin chaos is introduced to improve the random population initialization scheme of the CHOA,which not only guarantees the diversity of the population,but also enhances the distribution uniformity of the initial population.Next,Cauchy mutation is added to optimize the global search ability of the CHOA in the process of position(threshold)updating to avoid the CHOA falling into local optima.Finally,an improved CHOA was formed through the combination of chaos initialization and Cauchy mutation(CICMCHOA),then taking fuzzy Kapur as the objective function,this paper applied CICMCHOA to natural and medical image segmentation,and compared it with four algorithms,including the improved Satin Bowerbird optimizer(ISBO),Cuckoo Search(ICS),etc.The experimental results deriving from visual and specific indicators demonstrate that CICMCHOA delivers superior segmentation effects in image segmentation. 展开更多
关键词 Image segmentation image thresholding chimp optimization algorithm chaos initialization Cauchy mutation
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High-resolution Remote Sensing Image Segmentation Using Minimum Spanning Tree Tessellation and RHMRF-FCM Algorithm 认领 引用 被引量：12
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作者 Wenjie LIN Yu LI Quanhua ZHAO 《Journal of Geodesy and Geoinformation Science》 2020年第1期52-63,共12页
It is proposed a high resolution remote sensing image segmentation method which combines static minimum spanning tree(MST)tessellation considering shape information and the RHMRF-FCM algorithm.It solves the problems i... It is proposed a high resolution remote sensing image segmentation method which combines static minimum spanning tree(MST)tessellation considering shape information and the RHMRF-FCM algorithm.It solves the problems in the traditional pixel-based HMRF-FCM algorithm in which poor noise resistance and low precision segmentation in a complex boundary exist.By using the MST model and shape information,the object boundary and geometrical noise can be expressed and reduced respectively.Firstly,the static MST tessellation is employed for dividing the image domain into some sub-regions corresponding to the components of homogeneous regions needed to be segmented.Secondly,based on the tessellation results,the RHMRF model is built,and regulation terms considering the KL information and the information entropy are introduced into the FCM objective function.Finally,the partial differential method and Lagrange function are employed to calculate the parameters of the fuzzy objective function for obtaining the global optimal segmentation results.To verify the robustness and effectiveness of the proposed algorithm,the experiments are carried out with WorldView-3(WV-3)high resolution image.The results from proposed method with different parameters and comparing methods(multi-resolution method and watershed segmentation method in eCognition software)are analyzed qualitatively and quantitatively. 展开更多
关键词 static minimum spanning tree tessellation shape parameter RHMRF FCM algorithm high-resolution remote sensing image segmentation
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Alternative Fuzzy Cluster Segmentation of Remote Sensing Images Based on Adaptive Genetic Algorithm 认领 引用 被引量：1
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作者 WANG Jing TANG Jilong +3 位作者 LIU Jibin REN Chunying LIU Xiangnan FENG Jiang 《Chinese Geographical Science》 SCIE 2009年第1期83-88,共6页
Remote sensing image segmentation is the basis of image understanding and analysis. However,the precision and the speed of segmentation can not meet the need of image analysis,due to strong uncertainty and rich textur... Remote sensing image segmentation is the basis of image understanding and analysis. However,the precision and the speed of segmentation can not meet the need of image analysis,due to strong uncertainty and rich texture details of remote sensing images. We proposed a new segmentation method based on Adaptive Genetic Algorithm(AGA) and Alternative Fuzzy C-Means(AFCM) . Segmentation thresholds were identified by AGA. Then the image was segmented by AFCM. The results indicate that the precision and the speed of segmentation have been greatly increased,and the accuracy of threshold selection is much higher compared with traditional Otsu and Fuzzy C-Means(FCM) segmentation methods. The segmentation results also show that multi-thresholds segmentation has been achieved by combining AGA with AFCM. 展开更多
关键词 Adaptive Genetic Algorithm （AGA） Alternative Fuzzy C-Means （AFCM） image segmentation remote sensing
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Advanced Brain Tumor Segmentation in Magnetic Resonance Imaging via 3D U-Net and Generalized Gaussian Mixture Model-Based Preprocessing 认领 引用 被引量：2
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作者 Khalil Ibrahim Lairedj Zouaoui Chama +5 位作者 Amina Bagdaoui Samia Larguech Younes Menni Nidhal Becheikh Lioua Kolsi Badr M.Alshammari 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第8期2419-2443,共25页
Brain tumor segmentation from Magnetic Resonance Imaging(MRI)supports neurologists and radiologists in analyzing tumors and developing personalized treatment plans,making it a crucial yet challenging task.Supervised m... Brain tumor segmentation from Magnetic Resonance Imaging(MRI)supports neurologists and radiologists in analyzing tumors and developing personalized treatment plans,making it a crucial yet challenging task.Supervised models such as 3D U-Net perform well in this domain,but their accuracy significantly improves with appropriate preprocessing.This paper demonstrates the effectiveness of preprocessing in brain tumor segmentation by applying a pre-segmentation step based on the Generalized Gaussian Mixture Model(GGMM)to T1 contrastenhanced MRI scans from the BraTS 2020 dataset.The Expectation-Maximization(EM)algorithm is employed to estimate parameters for four tissue classes,generating a new pre-segmented channel that enhances the training and performance of the 3DU-Net model.The proposed GGMM+3D U-Net framework achieved a Dice coefficient of 0.88 for whole tumor segmentation,outperforming both the standard multiscale 3D U-Net(0.84)and MMU-Net(0.85).It also delivered higher Intersection over Union(IoU)scores compared to models trained without preprocessing or with simpler GMM-based segmentation.These results,supported by qualitative visualizations,suggest that GGMM-based preprocessing should be integrated into brain tumor segmentation pipelines to optimize performance. 展开更多
关键词 Magnetic resonance imaging(MRI) imaging technology GGMM EM algorithm 3D U-Net segmentation
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Double Enhanced Solution Quality Boosted RIME Algorithm with Crisscross Operations for Breast Cancer Image Segmentation 认领 引用 被引量：1
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作者 Mengjun Sun Yi Chen +3 位作者 Ali Asghar Heidari Lei Liu Huiling Chen Qiuxiang He 《Journal of Bionic Engineering》 SCIE EI CSCD 2024年第6期3151-3178,共28页
The persistently high incidence of breast cancer emphasizes the need for precise detection in its diagnosis.Computer-aided medical systems are designed to provide accurate information and reduce human errors,in which ... The persistently high incidence of breast cancer emphasizes the need for precise detection in its diagnosis.Computer-aided medical systems are designed to provide accurate information and reduce human errors,in which accurate and effective segmentation of medical images plays a pivotal role in improving clinical outcomes.Multilevel Threshold Image Segmentation(MTIS)is widely favored due to its stability and straightforward implementation.Especially when dealing with sophisticated anatomical structures,high-level thresholding is a crucial technique in identifying fine details.To enhance the accuracy of complex breast cancer image segmentation,this paper proposes an improved version of RIME optimizer EECRIME,denoted as the double Enhanced solution quality Crisscross RIME algorithm.The original RIME initially conducts an efficient optimization to target promising solutions.The double-enhanced solution quality(EESQ)mechanism is proposed for thorough exploitation without falling into local optimum.In contrast,the crisscross operations perform a further local exploration of the generated feasible solutions.The performance of EECRIME is verified with basic and advanced algorithms on IEEE CEC2017 benchmark functions.Furthermore,an EECRIME-based MTIS method in combination with Kapur’s entropy is applied to segment breast Infiltrating Ductal Carcinoma(IDC)histology images.The results demonstrate that the developed model significantly surpasses its competitors,establishing it as a practical approach for complex medical image processing. 展开更多
关键词 Rime optimization algorithm Double-enhanced solution quality mechanism Crisscross optimization algorithm Image segmentation Breast cancer
Image Segmentation of Brain MR Images Using Otsu’s Based Hybrid WCMFO Algorithm 认领 引用 被引量：6
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作者 A.Renugambal K.Selva Bhuvaneswari 《Computers, Materials & Continua》 SCIE EI 2020年第8期681-700,共20页
In this study,a novel hybrid Water Cycle Moth-Flame Optimization(WCMFO)algorithm is proposed for multilevel thresholding brain image segmentation in Magnetic Resonance(MR)image slices.WCMFO constitutes a hybrid betwee... In this study,a novel hybrid Water Cycle Moth-Flame Optimization(WCMFO)algorithm is proposed for multilevel thresholding brain image segmentation in Magnetic Resonance(MR)image slices.WCMFO constitutes a hybrid between the two techniques,comprising the water cycle and moth-flame optimization algorithms.The optimal thresholds are obtained by maximizing the between class variance(Otsu’s function)of the image.To test the performance of threshold searching process,the proposed algorithm has been evaluated on standard benchmark of ten axial T2-weighted brain MR images for image segmentation.The experimental outcomes infer that it produces better optimal threshold values at a greater and quicker convergence rate.In contrast to other state-of-the-art methods,namely Adaptive Wind Driven Optimization(AWDO),Adaptive Bacterial Foraging(ABF)and Particle Swarm Optimization(PSO),the proposed algorithm has been found to be better at producing the best objective function,Peak Signal-to-Noise Ratio(PSNR),Standard Deviation(STD)and lower computational time values.Further,it was observed thatthe segmented image gives greater detail when the threshold level increases.Moreover,the statistical test result confirms that the best and mean values are almost zero and the average difference between best and mean value 1.86 is obtained through the 30 executions of the proposed algorithm.Thus,these images will lead to better segments of gray,white and cerebrospinal fluid that enable better clinical choices and diagnoses using a proposed algorithm. 展开更多
关键词 Hybrid WCMFO algorithm Otsu’s function multilevel thresholding image segmentation brain MR image
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