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An adaptive position-guided gravitational search algorithm for function optimization and image threshold segmentation

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第1期121卷

作者： Guo, Anjing Wang, Yirui Guo, Lijun Zhang, Rong Yu, Yang Gao, Shangce Ningbo Univ Fac Elect Engn & Comp Sci Ningbo 315211 Zhejiang Peoples R China Nanjing Univ Posts & Telecommun Coll Automat & Coll Artificial Intelligence Nanjing 210023 Peoples R China Univ Toyama Fac Engn Toyama 9308555 Japan

Gravitational search algorithm is a population-based optimization method. To address its low search per-formance and premature convergence, a novel variant called adaptive position-guided gravitational search algorithm is proposed. It utilizes the best, worst and other particles' position information to adaptively determine the Kbest particles which provide a good movement direction. The gravitational force is reinforced by Kbest particles and new constructed Dbest particles to improve the exploration and exploitation abilities. Various particles' position information jointly provide the effective search guideline and accelerate the convergence rate. Validations are conducted to firstly discuss the parameters and strategies of the proposed algorithm. Then, compared with several state-of-the-art gravitational search algorithm variants on CEC2017 benchmark functions, the proposed algorithm proves its superiority. Finally, the proposed algorithm exhibits the good segmentation effect on image threshold segmentation problems.

关键词： Gravitational search algorithm Position Exploration and exploitation Function optimization image threshold segmentation

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image threshold segmentation Based on An Improved Bee Colony Algorithm 8

Image Threshold Segmentation Based on An Improved Bee Colony...

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8th International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC)

作者： Huo Fengcai Wang Di Ren Weijian Heilongjiang Prov Key Lab Networking & Intelligen Daqing Peoples R China Northeast Petr Univ Dept Elect Informat Engn Daqing Peoples R China

ISBN: (纸本)9781538682463

image segmentation means that the image is divided into specific and unique regions. There are many existing image segmentation methods, and the threshold-based segmentation method is widely applied because of its easy implementation, simplicity and high efficiency. In this paper, artificial bee colony algorithm is applied to image threshold segmentation. Kapur entropy is used as a fitness function, the artificial bee colony algorithm is improved through the adaptive scaling factor. The search area is enlarged through the large-scale factor, the neighborhood search scope is reduced through the small-scale factor and the search efficiency is enhanced. Finally, by comparing the PSNR values of the image, the algorithm has a good segmentation effect and good convergence performance.

关键词： image threshold segmentation artificial bee colony algorithm improved artificial bee colony algorithm Kapur entropy

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Bloch quantum artificial bee colony algorithm and its application in image threshold segmentation

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SIGNAL image AND VIDEO PROCESSING 2017年第8期11卷 1585-1592页

作者： Huo, Fengcai Liu, Yang Wang, Di Sun, Baoxiang Northeast Petr Univ Dept Elect Informat Engn Daqing 163318 Heilongjiang Peoples R China

image segmentation is a key step in many computer vision applications. threshold-based methods are widely used for image segmentation. The core problem of threshold segmentation is how to get a reasonable threshold. An improved algorithm of Bloch spherical coordinates for quantum artificial bee colony is proposed in this paper. The two-dimensional linear cross-entropy of the image is used as a fitness function. Meanwhile, the image threshold segmentation problem is researched with BQABC. Firstly, quantum computation is introduced and the quantum artificial bee colony algorithm is improved. Secondly, the improved quantum artificial bee colony algorithm is applied to image threshold segmentation. Finally, the OTSU algorithm, ME method, MEM, ABC algorithm and BQABC algorithm are applied to the threshold segmentation of standard images and network image. The response curve and performance index of five algorithms are compared and analyzed. The experimental results show that the BQABC algorithm can obtain the segmentation threshold and it has a good image segmentation effect accurately and quickly.

关键词： Bloch spherical Quantum computation BQABC image threshold segmentation

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Multilevel threshold segmentation of rice plant images utilizing tuna swarm optimization algorithm incorporating quadratic interpolation and elite swarm genetic operators

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 263卷

作者： Wang, Wentao Ye, Chen Pan, Zhongjie Tian, Jun Nankai Univ Coll Software Tianjin 300350 Peoples R China Jiangxi Agr Univ Sch Comp & Informat Engn Nanchang 330045 Peoples R China

Rice plant images exhibit varying texture characteristics across different growth stages and environmental conditions. A suitable image thresholding segmentation method can effectively separate the different feature regions of rice plants for better monitoring of rice growth to improve yield. This paper employs the Sigmoid nonlinear weights strategy, Quadratic interpolation strategy, and elite swarm Genetic strategy to enhance distinct stages of the Tuna Swarm Optimization algorithm (TSO) to propose the SQGTSO algorithm, which has better convergence and global optimization capability. 10 CEC2017 benchmark functions are selected to validate the performance of the SQGTSO algorithm, and the experimental results show that the SQGTSO algorithm outperforms the other algorithms in 9 benchmark functions. To assess the feasibility and efficacy of the SQGTSO for multilevel threshold segmentation of rice plant images, this paper selects 8 rice plant images with diverse styles for the design of two sets of comparative experiments. The SQGTSO algorithm is comprehensively benchmarked against seven advanced metaheuristic algorithms and one machine learning method. Under the conditions of threshold levels ranging from 4 to 30, two distinct experiment sets are devised. In each set, Otsu's method and the MCET method are employed as fitness functions for the metaheuristic algorithms, respectively. The assessment criteria include fitness values, PSNR, SSIM, FSIM and HPSI. Additionally, the Friedman method is utilized for statistical analysis of the five metrics yielded by each algorithm. The experimental findings demonstrate the significant advantages of the SQGTSO method concerning five evaluation metrics and its convergence performance compared to other competitors.

关键词： Rice plant image image threshold segmentation Metaheuristic algorithm Tuna swarm optimization algorithm Elite swarm group strategy

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SERS mapping combined with chemometrics, for accurate quantification of methotrexate from patient samples

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024年 305卷 123536页

作者： He, Peihuan Dumont, Elodie Goksela, Yaman Slipets, Roman Schmiegelow, Kjeld Chen, Quansheng Zor, Kinga Boisen, Anja Tech Univ Denmark Dept Hlth Technol Ctr Intelligent Drug Delivery & Sensing Using Micr DK-2800 Lyngby Denmark BioInnovat Inst Fdn DK-2200 Copenhagen N Denmark Rigshospitalet Univ Hosp Dept Pediat & Adolescent Med DK-2100 Copenhagen Denmark Jiangsu Univ Sci & Technol Sch Grain Sci & Technol Zhenjiang 212100 Peoples R China Jimei Univ Coll Ocean Food & Biol Engn Xiamen 361021 Peoples R China Jiangsu Univ Sch Food & Biol Engn Zhenjiang 212013 Peoples R China Jiangsu Univ Sci & Technol Sch Mat Sci & Engn Zhenjiang 212100 Peoples R China Tech Univ Denmark DK-2800 Lyngby Denmark

Despite the technological development in Raman instrumentation that has democratized access to 2D sample scanning capabilities, most quantitative surface-enhanced Raman scattering (SERS) analyses are still performed by only acquiring a single or a few spectra per sample and performing univariate data analysis on those. This strategy can however reach its limit when analytes need to be detected and quantified in complex matrices. In that case, surface fouling and competition between the target analyte and interfering compounds can impair univariate SERS data analysis, underlining the need for a more in-depth data analysis strategy based on exploiting of full-spectrum *** this paper, a multivariate data analysis strategy was developed, for analyzing SERS maps of methotrexate (MTX) from patient samples, including all steps from baseline correction, selection of wavelength, and the relevant pixels in the map (image threshold segmentation), as well as quantitative model construction based on partial-least squares *** the different baseline correction methods evaluated, standard normal variable transformation and Savitzky-Golay smoothing proved to be more suitable, while the genetic algorithm wavelength screening method was able to screen out MTX-related SERS spectral regions more efficiently. Importantly, with the here-developed process, it was sufficient to use MTX-spiked commercial serum when building quantitative models, removing the need to work with MTX-spiked patient samples, and consequently enabling time-and resource-saving quantitative analyses. Besides, the developed multivariate data analysis approach showed superior performances compared with univariate analysis, with 30 % improved sensitivity (detection limit of 5.7 mu M), 25 % higher reproducibility (average relative standard variation of 15.6 %), and 110 % better accuracy (average prediction error of-10.5 %).

关键词： SERS mapping Genetic algorithm (GA) image threshold segmentation Methotrexate (MTX) Patient samples Multivariate quantitative data analysis

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Effect of Soil Conditioning on Saturated Sand Layers in EPB Shields: A Microstructural Analysis Based on CT Scanning and SEM

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022年第10期47卷 12387-12397页

作者： Yang, Zhiyong Yang, Xing Ding, Yanjie Jiang, Yusheng Shao, Xiaokang Qi, Weiqiang Liu, Nan China Univ Min & Technol Beijing Sch Mech & Civil Engn Beijing 100083 Peoples R China

Soil conditioning of an earth pressure balance shield in a saturated sand layer aims to impart adequate flow plasticity and impermeability. Thus, the effect of soil conditioning on the saturated sand layer was analyzed for Harbin Metro Line 2 in the tunnel section project from Harbin North Station to Dagengjia Station, and the optimal soil conditioning ratio was obtained through experiments on medium sand using foam, bentonite, and carboxymethyl cellulose. In addition, the microscopic pores and shape characteristics of the soil before and after conditioning were compared using computerized tomography (CT) scanning and scanning electron microscopy (SEM) to evaluate the microscopic effect of soil conditioning. The current research results revealed that the soil conditioning reduced the particle size dispersion and improved the particle gradation, and the conditioned soil particles were more uniform than the original soil sample. Moreover, the CT scanning images processed using the threshold method illustrated that the pores of the conditioned soil were independent of each other, no continuous channels were formed, and the porosity of each layer was under 4%. In addition, the SEM images confirmed that the clay mineral composition improved the angularity and sharpness of the sand particles, and the flocculent aggregate structure, formed in combination with the sand particles, reduced the soil permeability. Furthermore, the practical application of the optimal ratio of conditioning materials, obtained experimentally and verified through the aforementioned observation, yielded proper results in the field construction, and the spewing of the screw conveyor during the shield tunneling process was avoided.

关键词： EPB shield Soil conditioning test image threshold segmentation Microscopic pore characteristics Microscopic shape

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The Reliability of CBCT to Assess Quality of Augmented Bone After Lateral Sinus Floor Elevation With Xenografts: A Retrospective Analysis

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Clinical Implant Dentistry and Related Research 2025年第3期27卷 e70029页

作者： Wang, Xixuan Zhao, Wenyan Liao, Menglin Liu, Yunfei Ban, Chengzhe Fu, Gang Wu, Qingqing Department of Oral Implantology Stomatological Hospital of Chongqing Medical University Chongqing China Department of Oral Implantology Ruitai Stomatological Hospital Chongqing China

Objetives: This study aimed to explore the reliability of cone beam computed tomography (CBCT) in evaluating the quality of augmented bone after lateral sinus floor elevation (LSFE) with xenografts. Materials and Methods: Thirty-six patients with lost maxillary molars were included, with half of whom received LSFE with xenografts and staged implant placement, and the other half showed no vertical bone defects and underwent implant placement directly. A total of 36 implants were included, with 18 implants in each group. A CBCT exam was taken before implant placement to acquire data on mineral quality at the future implant site, including bone mineral density (BMD), various microstructure indices, and gray values (GVs) within different threshold ranges. Augmented bone biopsies were collected during implant preparation. The microstructure indices and histological characteristics of the biopsies were evaluated by micro computed tomography (μCT) and histological staining. An implant-oriented volume of interest for CBCT analysis was established to co-locate the CBCT-measured data and the biopsy-related data using 3DSlicer. A Spearman rank correlation test was used to analyze the relationship between CBCT-measured data and the biopsy-related data. Results: μCT-measured microstructure indices of the augmented bone (BV/TV and ***) were significantly correlated with new bone area (BV/TV, p = 0.035, r = 0.498;***, p = 0.027, r = 0.520). No correlation was found between the CBCT-measured and μCT-measured microstructure indices. CBCT-measured BMD and microstructure indices hardly showed any correlation with histological indices (p > 0.05). When the threshold was set from 0 to 50, the mean GVs were significantly, positively correlated with new bone area (p = 0.041, r = 0.486), and bone substitute area was positively correlated to the mean GVs of higher threshold (range 60–255, p = 0.048, r = 0.472;range 70–255, p = 0.009, r = 0.593). Conclusions: CBCT without bone substitute

关键词： bone quality CBCT image threshold segmentation lateral sinus floor elevation xenogenic bone substitute

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Preaching-inspired swarm intelligence algorithm and its applications

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KNOWLEDGE-BASED SYSTEMS 2021年 211卷 106552-106552页

作者： Wei, Dong Wang, Zhongbin Si, Lei Tan, Chao China Univ Min & Technol Sch Mech Engn 1 Daxue Rd Xuzhou Jiangsu Peoples R China China Univ Min & Technol Jiangsu Key Lab Mine Mech & Elect Equipment 1 Daxue Rd Xuzhou Jiangsu Peoples R China

Swarm intelligence algorithms have been widely used in both research and engineering fields, but they face the problems of low accuracy and premature convergence, which limit their further applications. Inspired by the preachers' social behaviors, a novel meta-heuristic swarm intelligence algorithm, Preaching Optimization Algorithm, is proposed in this paper. Its convergence accuracy is effectively improved by improving the initial range of offspring individuals. Meanwhile, by introducing the combined weight including individual fitness and position relationship between individuals, the diversity of individuals is improved, thus reducing the possibility of algorithm premature convergence. In this paper, the parameter sensitivity of the Preaching Optimization Algorithm is analyzed firstly. Secondly, the proposed algorithm is evaluated by comparing it with the other meta-heuristic algorithms on CEC'17 benchmark functions. The results indicate the proposed algorithm has strong competitiveness both accuracy and robustness in solving optimization problems. Finally, the Preaching Optimization Algorithm is used to solve the typical problems in engineering and image threshold segmentation, which further verifies the excellent optimization performance of the proposed algorithm. In this paper, the Preaching Optimization Algorithm is explained in detail and compared with other existing methods to evaluate its comprehensive performance. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Swarm intelligence Meta-heuristic algorithm Typical engineering applications image threshold segmentation

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Brain tissue classification method for clinical decision-support systems

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2017年 64卷 232-241页

作者： Guo, Peifang Sherbrooke St Montreal PQ H4B 1R6 Canada

Studies have shown that the degree of atrophy for the brain tissue volumes could provide an indicator of disease progression for patients with age-related dementia. In this paper, the proposed method for joint tissue segmentation and removal of non-brain tissues during post-processing is carried out in magnetic resonance imaging (MRI) by integrating the models of threshold segmentation and Post-processing Pipeline (TS-PP), based on greyscale histograms. In the approach, we employ the coefficient of variance statistically to determine the dual thresholds in the intensities of grey matter and white matter, and then use them to obtain preliminary thresholded masks, which consist of some non-brain tissues on skull. Different from other popular methods relied on pre-processing steps to be performed first for removal of non-brain tissues before segmentation, the TS PP selects thresholds by the coefficient of variance for segmentation first, and then performs two groups of operations during post-processing,, iterative contour refinement and morphological reconstruction, in order to minimize non-brain tissues on skull. In the validation, the TS PP is implemented using 20 simulated Ti-weighted MRI datasets and a real-time OASIS data. The experimental results demonstrate the robustness of the approach, compared to some existing segmentation methods building upon the pre-processing steps. In comparison, the proposed hybrid model TS PP achieves an improved performance in tissue classification in MRI. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： image threshold segmentation image intensity modelling Contour extraction Morphological reconstruction Dice similarity Greyscale conversion Magnetic resonance imaging

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OIL DEPOTS DETECTION FROM HIGH RESOLUTION REMOTE SENSING imageS BASED ON SALIENT REGION EXTRACTION 5

OIL DEPOTS DETECTION FROM HIGH RESOLUTION REMOTE SENSING IMA...

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5th International Conference on Audio, Language and image Processing (ICALIP)

作者： Li, Chaoyang Huo, Hong Fang, Tao Shanghai Jiao Tong Univ Dept Automat Shanghai Peoples R China

ISBN: (纸本)9781509006540

The traditional methods of detecting oil depots usually use Hough transform and template matching, which often have lower detection rates and are difficult to implement. An efficient two-step detection framework is proposed in this paper to detect oil depots in high resolution remote sensing images. In the first stage, LC saliency model is used to detect the salient regions and shows a good performance on highlighting oil depots. In the second stage, task related targets from these salient regions are extracted by removing the irrelevant salient areas according to the special properties of the targets. According to the final shape, the area and distribution of oil depots, using image threshold segmentation and the graph-based clustering procedure, oil depots are detected with fairly good accuracy and efficiency.

关键词： Saliency detection image threshold segmentation graph-based clustering oil depots

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