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Journal articles on the topic 'Multilevel Thresholding'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Khairuzzaman, Abdul Kayom Md, and Saurabh Chaudhury. "Moth-Flame Optimization Algorithm Based Multilevel Thresholding for Image Segmentation." International Journal of Applied Metaheuristic Computing 8, no. 4 (2017): 58–83. http://dx.doi.org/10.4018/ijamc.2017100104.

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Multilevel thresholding is a popular image segmentation technique. However, computational complexity of multilevel thresholding increases very rapidly with increasing number of thresholds. Metaheuristic algorithms are applied to reduce computational complexity of multilevel thresholding. A new method of multilevel thresholding based on Moth-Flame Optimization (MFO) algorithm is proposed in this paper. The goodness of the thresholds is evaluated using Kapur's entropy or Otsu's between class variance function. The proposed method is tested on a set of benchmark test images and the performance is

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2

Wang, Yi, and Kangshun Li. "A Fuzzy Adaptive Firefly Algorithm for Multilevel Color Image Thresholding Based on Fuzzy Entropy." International Journal of Cognitive Informatics and Natural Intelligence 15, no. 4 (2021): 1–20. http://dx.doi.org/10.4018/ijcini.20211001.oa44.

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Multilevel thresholding image segmentation has always been a hot issue of research in last several years since it has a plenty of applications. Traditional exhaustive search method consumes a lot of time for searching the optimal multilevel thresholding, color images contain more information, solving multilevel thresholding will become worse. However, the meta-heuristic search algorithm has unique advantages in solving multilevel threshold values. In this paper, a fuzzy adaptive firefly algorithm (FaFA) is proposed to solve the optimal multilevel thresholding for color images, and the fuzzy Ka

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Khairuzzaman, Abdul Kayom Md, and Saurabh Chaudhury. "Brain MR Image Multilevel Thresholding by Using Particle Swarm Optimization, Otsu Method and Anisotropic Diffusion." International Journal of Applied Metaheuristic Computing 10, no. 3 (2019): 91–106. http://dx.doi.org/10.4018/ijamc.2019070105.

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Multilevel thresholding is widely used in brain magnetic resonance (MR) image segmentation. In this article, a multilevel thresholding-based brain MR image segmentation technique is proposed. The image is first filtered using anisotropic diffusion. Then multilevel thresholding based on particle swarm optimization (PSO) is performed on the filtered image to get the final segmented image. Otsu function is used to select the thresholds. The proposed technique is compared with standard PSO and bacterial foraging optimization (BFO) based multilevel thresholding techniques. The objective image quali

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Yin, Peng-Yeng, and Ling-Hwei Chen. "Random-sampling thresholding: A new approach to multilevel thresholding." Signal Processing 34, no. 3 (1993): 311–22. http://dx.doi.org/10.1016/0165-1684(93)90138-z.

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Dash, Prasannajit, and Maya Nayak. "Multilevel Thresholding using PSO Clustering." International Journal of Computer Applications 97, no. 18 (2014): 27–32. http://dx.doi.org/10.5120/17107-7752.

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6

Cao, L., Z. K. Shi, and E. K. W. Cheng. "Fast automatic multilevel thresholding method." Electronics Letters 38, no. 16 (2002): 868. http://dx.doi.org/10.1049/el:20020594.

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7

Hertz, Lois, and Ronald W. Schafer. "Multilevel thresholding using edge matching." Computer Vision, Graphics, and Image Processing 44, no. 3 (1988): 279–95. http://dx.doi.org/10.1016/0734-189x(88)90125-9.

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Abualigah, Laith, Ali Diabat, Putra Sumari, and Amir H. Gandomi. "A Novel Evolutionary Arithmetic Optimization Algorithm for Multilevel Thresholding Segmentation of COVID-19 CT Images." Processes 9, no. 7 (2021): 1155. http://dx.doi.org/10.3390/pr9071155.

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One of the most crucial aspects of image segmentation is multilevel thresholding. However, multilevel thresholding becomes increasingly more computationally complex as the number of thresholds grows. In order to address this defect, this paper proposes a new multilevel thresholding approach based on the Evolutionary Arithmetic Optimization Algorithm (AOA). The arithmetic operators in science were the inspiration for AOA. DAOA is the proposed approach, which employs the Differential Evolution technique to enhance the AOA local research. The proposed algorithm is applied to the multilevel thresh

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Khairuzzaman, Abdul Kayom Md, and Saurabh Chaudhury. "Modified Moth-Flame Optimization Algorithm-Based Multilevel Minimum Cross Entropy Thresholding for Image Segmentation." International Journal of Swarm Intelligence Research 11, no. 4 (2020): 123–39. http://dx.doi.org/10.4018/ijsir.2020100106.

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Multilevel thresholding is a widely used image segmentation technique. However, multilevel thresholding becomes more and more computationally expensive as the number of thresholds increase. Therefore, it is essential to incorporate some suitable optimization technique to make it practical. In this article, a modification is proposed to the Moth-Flame Optimization (MFO) algorithm and then it is applied to multilevel thresholding for image segmentation. Cross entropy is used as the objective function to select the optimal thresholds. A set of benchmark test images are used to evaluate the propos

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S, Anbazhagan. "APPLICATION OF TEACHING LEARNING BASED OPTIMIZATION IN MULTILEVEL IMAGE THRESHOLDING." ICTACT Journal on Image and Video Processing 11, no. 4 (2021): 2413–22. http://dx.doi.org/10.21917/ijivp.2021.0344.

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This paper proposes a Teaching learning-based optimization (TLBO) algorithm for the multilevel image thresholding using Kapur entropy. In image processing, the thresholding arises to help medical imaging, detection, and recognition in making an informed decision about the image. However, they are computationally expensive reaching out to multilevel thresholding since they thoroughly search the optimal thresholds to enhance the fitness functions. In order to validate the chaotic characteristic of multilevel thresholding, a TLBO algorithm is modeled. The proposed model is an algorithm-specific,

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11

Dahiya, Anju, and R. B Dubey. "Survey of Some Multilevel Thresholding Techniques for Medical Imaging." International Journal of Scientific Engineering and Research 3, no. 7 (2015): 103–6. https://doi.org/10.70729/10071501.

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12

He, Lifang, and Songwei Huang. "Improved Glowworm Swarm Optimization Algorithm for Multilevel Color Image Thresholding Problem." Mathematical Problems in Engineering 2016 (2016): 1–24. http://dx.doi.org/10.1155/2016/3196958.

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The thresholding process finds the proper threshold values by optimizing a criterion, which can be considered as a constrained optimization problem. The computation time of traditional thresholding techniques will increase dramatically for multilevel thresholding. To greatly overcome this problem, swarm intelligence algorithm is widely used to search optimal thresholds. In this paper, an improved glowworm swarm optimization (IGSO) algorithm has been presented to find the optimal multilevel thresholds of color image based on the between-class variance and minimum cross entropy (MCE). The propos

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13

Prahara, Murinto, and Erik Ujianto. "Multilevel Thresholding Image Segmentation Based-Logarithm Decreasing Inertia Weight Particle Swarm Optimization." International Journal of Advances in Soft Computing and its Applications 14, no. 3 (2022): 65–77. http://dx.doi.org/10.15849/ijasca.221128.05.

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Abstract The image segmentatation technique that is often used is thresholding. Image segmentation is a process of dividing the image into different regions according to their similar characteristics. This research proposes a multilevel thresholding algorithm using modified particle swarm optimization to solve a segmentation problem. The threshold optimal values are determined by maximizing Otsu’s objective function using optimization technique namely particle swarm optimization based on the logarithmic decreasing inertia weight (LogDIWPSO). The proposed method reduces the computational time t

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Rahkar Farshi, Taymaz, and Recep Demirci. "Multilevel image thresholding with multimodal optimization." Multimedia Tools and Applications 80, no. 10 (2021): 15273–89. http://dx.doi.org/10.1007/s11042-020-10432-4.

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Cheng, H. D., C. H. Chen, H. H. Chiu, and Huijuan Xu. "Fuzzy homogeneity approach to multilevel thresholding." IEEE Transactions on Image Processing 7, no. 7 (1998): 1084–86. http://dx.doi.org/10.1109/83.701171.

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Małyszko, Dariusz, and Jarosław Stepaniuk. "Adaptive multilevel rough entropy evolutionary thresholding." Information Sciences 180, no. 7 (2010): 1138–58. http://dx.doi.org/10.1016/j.ins.2009.11.034.

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17

Iberahim, Zuraila, Mohd Zamzuri Mohammad Zain, Abdul Halim Ismail, et al. "Analysis of Multilevel Otsu Thresholding for Corrosion Rate of Anodised AZ91D Magnesium Alloy ." Journal of Engineering and Science Research 6, no. 5 (2022): 6–11. http://dx.doi.org/10.26666/rmp.jesr.2022.5.2.

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Magnesium and its alloy are renowned for their high strength-to-weight ratio, excellent machinability and good castability, making it a favourable material for manufacturing various automotive parts. But when exposed to the environment, magnesium and its alloy have a weak ability to resist corrosion. AZ91D magnesium alloy was anodised and immersed in a salt solution to accelerate deterioration. The image of the corroded surface resulting from the immersion test was analysed by quantising the corroded regions. This paper studies the feasibility of an image processing approach using the multilev

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18

Ouadfel, Salima, and Abdelmalik Taleb-Ahmed. "Performance Study of Harmony Search Algorithm for Multilevel Thresholding." Journal of Intelligent Systems 25, no. 4 (2016): 473–513. http://dx.doi.org/10.1515/jisys-2014-0147.

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AbstractThresholding is the easiest method for image segmentation. Bi-level thresholding is used to create binary images, while multilevel thresholding determines multiple thresholds, which divide the pixels into multiple regions. Most of the bi-level thresholding methods are easily extendable to multilevel thresholding. However, the computational time will increase with the increase in the number of thresholds. To solve this problem, many researchers have used different bio-inspired metaheuristics to handle the multilevel thresholding problem. In this paper, optimal thresholds for multilevel

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19

P.D., Sathya. "IMAGE SEGMENTATION WITH IMPROVED BACTERIAL FORAGING ALGORITHM." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES5 5, no. 3 (2018): 110–18. https://doi.org/10.5281/zenodo.1404195.

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Image thresholding is an important technique for image processing and pattern recognition. Multilevel thresholding problem is often treated as a problem of optimization of an objective function. In this paper, minimum cross entropy (MCE) is introduced for multilevel thresholding which uses Improved Bacterial Foraging (IBF) algorithm for minimizing the MCE objective function. Some examples of test images are presented to compare the segmentation methods based on the IBF approach, with bacterial foraging (BF) algorithm, particle swarm optimization (PSO) algorithm and genetic algorithm (GA). From

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P.D., Sathya. "MINIMUM CROSS ENTROPY BASED IMAGE SEGMENTATION USING NEW HEURISTIC OPTIMIZATION TECHNIQUE." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 5, no. 7 (2019): 522–30. https://doi.org/10.5281/zenodo.2656443.

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Image thresholding is an important technique for image processing and pattern recognition. Multilevel thresholding problem is often treated as a problem of optimization of an objective function. In this paper, minimum cross entropy (MCE) is introduced for multilevel thresholding which uses Improved Bacterial Foraging (IBF) algorithm for minimizing the MCE objective function. Some examples of test images are presented to compare the segmentation methods based on the IBF approach, with bacterial foraging (BF) algorithm, particle swarm optimization (PSO) algorithm and genetic algorithm (GA). From

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21

Ben Miled, Achraf, Mohammed Ahmed Elhossiny, Marwa Anwar Ibrahim Elghazawy, Ashraf F. A. Mahmoud, and Faroug A. Abdalla. "Enhanced Chaos Game Optimization for Multilevel Image Thresholding through Fitness Distance Balance Mechanism." Engineering, Technology & Applied Science Research 14, no. 4 (2024): 14945–55. http://dx.doi.org/10.48084/etasr.7713.

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This study proposes a method to enhance the Chaos Game Optimization (CGO) algorithm for efficient multilevel image thresholding by incorporating a fitness distance balance mechanism. Multilevel thresholding is essential for detailed image segmentation in digital image processing, particularly in environments with complex image characteristics. This improved CGO algorithm adopts a hybrid metaheuristic framework that effectively addresses the challenges of premature convergence and the exploration-exploitation balance, typical of traditional thresholding methods. By integrating mechanisms that b

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22

Wang, Chunzhi, Chengkun Tu, Siwei Wei, Lingyu Yan, and Feifei Wei. "MSWOA: A Mixed-Strategy-Based Improved Whale Optimization Algorithm for Multilevel Thresholding Image Segmentation." Electronics 12, no. 12 (2023): 2698. http://dx.doi.org/10.3390/electronics12122698.

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Multilevel thresholding image segmentation is one of the most widely used segmentation methods in the field of image segmentation. This paper proposes a multilevel thresholding image segmentation technique based on an improved whale optimization algorithm. The WOA has been applied to many complex optimization problems because of its excellent performance; however, it easily falls into local optimization. Therefore, firstly, a mixed-strategy-based improved whale optimization algorithm (MSWOA) is proposed using the k-point initialization algorithm, the nonlinear convergence factor, and the adapt

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23

Ouadfel, Salima, and Souham Meshoul. "Nature-Inspired Metaheuristics for Automatic Multilevel Image Thresholding." International Journal of Applied Metaheuristic Computing 5, no. 4 (2014): 47–69. http://dx.doi.org/10.4018/ijamc.2014100103.

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Thresholding is one of the most used methods of image segmentation. It aims to identify the different regions in an image according to a number of thresholds in order to discriminate objects in a scene from background as well to distinguish objects from each other. A great number of thresholding methods have been proposed in the literature; however, most of them require the number of thresholds to be specified in advance. In this paper, three nature-inspired metaheuristics namely Artificial Bee Colony, Cuckoo Search and Bat algorithms have been adapted for the automatic multilevel thresholding

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24

Ji, Wenqi, and Xiaoguang He. "Kapur's entropy for multilevel thresholding image segmentation based on moth-flame optimization." Mathematical Biosciences and Engineering 18, no. 6 (2021): 7110–42. http://dx.doi.org/10.3934/mbe.2021353.

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<abstract> <p>Multilevel thresholding is a reliable and efficacious method for image segmentation that has recently received widespread recognition. However, the computational complexity of the multilevel thresholding method increases as the threshold level increases, which causes the low segmentation accuracy of this method. To overcome this shortcoming, this paper presents a moth-flame optimization (MFO) established on Kapur's entropy to clarify the multilevel thresholding image segmentation. The MFO adjusts exploration and exploitation to achieve the best fitness value. To valid

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Bouteldja, Mohamed Abdou, Mohamed Baadeche, and Mohamed Batouche. "Multilevel Thresholding for Image Segmentation Based on Cellular Metaheuristics." International Journal of Applied Metaheuristic Computing 9, no. 4 (2018): 1–32. http://dx.doi.org/10.4018/ijamc.2018100101.

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This article describes how multilevel thresholding image segmentation is a process used to partition an image into well separated regions. It has various applications such as object recognition, edge detection, and particle counting, etc. However, it is computationally expensive and time consuming. To alleviate these limitations, nature inspired metaheuristics are widely used to reduce the computational complexity of such problem. In this article, three cellular metaheuristics namely cellular genetic algorithm (CGA), cellular particle swarm optimization (CPSO) and cellular differential evoluti

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Olugbara, Oludayo O., Emmanuel Adetiba, and Stanley A. Oyewole. "Pixel Intensity Clustering Algorithm for Multilevel Image Segmentation." Mathematical Problems in Engineering 2015 (2015): 1–19. http://dx.doi.org/10.1155/2015/649802.

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Image segmentation is an important problem that has received significant attention in the literature. Over the last few decades, a lot of algorithms were developed to solve image segmentation problem; prominent amongst these are the thresholding algorithms. However, the computational time complexity of thresholding exponentially increases with increasing number of desired thresholds. A wealth of alternative algorithms, notably those based on particle swarm optimization and evolutionary metaheuristics, were proposed to tackle the intrinsic challenges of thresholding. In codicil, clustering base

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Zhang, Chuang, Yue-Han Pei, Xiao-Xue Wang, Hong-Yu Hou, and Li-Hua Fu. "Symmetric cross-entropy multi-threshold color image segmentation based on improved pelican optimization algorithm." PLOS ONE 18, no. 6 (2023): e0287573. http://dx.doi.org/10.1371/journal.pone.0287573.

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To address the problems of low accuracy and slow convergence of traditional multilevel image segmentation methods, a symmetric cross-entropy multilevel thresholding image segmentation method (MSIPOA) with multi-strategy improved pelican optimization algorithm is proposed for global optimization and image segmentation tasks. First, Sine chaotic mapping is used to improve the quality and distribution uniformity of the initial population. A spiral search mechanism incorporating a sine cosine optimization algorithm improves the algorithm’s search diversity, local pioneering ability, and convergenc

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Karri, Chiranjeevi, G. Ramesh Babu, P. M. K. Prasad, and M. S. R. Naidu. "Novel 2-D Histogram-Based Soft Thresholding for Brain Tumor Detection and Image Compression." International Journal of Applied Metaheuristic Computing 13, no. 1 (2022): 1–21. http://dx.doi.org/10.4018/ijamc.292497.

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The objective of image compression is to extract meaningful clusters in a given image. Significant groups are possible with absolute threshold values. 1-D histogram-based multilevel thresholding is computationally complex and reconstructed image visual quality comparatively low because of equal distribution of energy over the entire histogram plan. So, 2-D histogram-based multilevel thresholding is proposed in this paper by maximizing the Renyi entropy with a novel hybrid Genetic Algorithm, Particle Swarm Optimization and Symbiotic Organisms Search (hGAPSO-SOS), and the obtained results are co

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Singh, V. P., Tapan Prakash, Natwar Singh Rathore, Dharma Pal Singh Chauhan, and Sugandh P. Singh. "Multilevel Thresholding with Membrane Computing Inspired TLBO." International Journal on Artificial Intelligence Tools 25, no. 06 (2016): 1650030. http://dx.doi.org/10.1142/s0218213016500305.

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The selection of optimal thresholds is still a challenging task for researchers in case of multilevel thresholding. Many swarm and evolutionary computation techniques have been applied for obtaining optimal values of thresholds. The performance of all these computation techniques is highly dependent on proper selection of algorithm-specific parameters. In this work, a new hybrid optimization technique, membrane computing inspired teacher-learner-based-optimization (MCTLBO), is proposed which is based on the structure of membrane computing (MC) and teacher-learner-based-optimization (TLBO) algo

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30

Sharma, Suvita Rani, Samah Alshathri, Birmohan Singh, Manpreet Kaur, Reham R. Mostafa, and Walid El-Shafai. "Hybrid Multilevel Thresholding Image Segmentation Approach for Brain MRI." Diagnostics 13, no. 5 (2023): 925. http://dx.doi.org/10.3390/diagnostics13050925.

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A brain tumor is an abnormal growth of tissues inside the skull that can interfere with the normal functioning of the neurological system and the body, and it is responsible for the deaths of many individuals every year. Magnetic Resonance Imaging (MRI) techniques are widely used for detection of brain cancers. Segmentation of brain MRI is a foundational process with numerous clinical applications in neurology, including quantitative analysis, operational planning, and functional imaging. The segmentation process classifies the pixel values of the image into different groups based on the inten

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31

Shang, Caijie, Dong Zhang, and Yan Yang. "A gradient-based method for multilevel thresholding." Expert Systems with Applications 175 (August 2021): 114845. http://dx.doi.org/10.1016/j.eswa.2021.114845.

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32

Ameer, Salah. "Multilevel Image Thresholding Using the Complement Feature." American Journal of Engineering and Applied Sciences 13, no. 3 (2020): 426–30. http://dx.doi.org/10.3844/ajeassp.2020.426.430.

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Sabeena, R., and C. Subassree. "MULTILEVEL IMAGE THRESHOLDING USING ARTIFICIAL INTELLIGENCE TECHNIQUES." International Journal of Advanced Research 5, no. 5 (2017): 94–100. http://dx.doi.org/10.21474/ijar01/4083.

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34

ZHENG, LIYING, KUIFENG LIU, and LEI YU. "MULTILEVEL THRESHOLDING METHOD BASED ON NORMALIZED CUT." International Journal of Image and Graphics 09, no. 04 (2009): 531–40. http://dx.doi.org/10.1142/s0219467809003563.

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Based on valleys of the histogram of an image as well as the normalized cut (Ncut) partitioning method, a novel multilevel thresholding technique with low computational complexity has been developed in this paper. A wavelet transform is first adopted to reduce the length of the original histogram, and to smoothen the histogram. All the valleys of the reduced histogram are then regarded as candida te thresholds based on which a weighted undirected graph is constructed. An Ncut criteria and a refinement procedure are finally adopted to determine the optimal thresholds. A comparison between the p

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35

LI, Gang, Xinbo ZHENG, and Zhaocheng YANG. "Pedometer method based on adaptive multilevel thresholding." Journal of Shenzhen University Science and Engineering 35, no. 2 (2018): 158. http://dx.doi.org/10.3724/sp.j.1249.2018.02158.

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36

Eichmann, Marco. "Framework for efficient optimal multilevel image thresholding." Journal of Electronic Imaging 18, no. 1 (2009): 013004. http://dx.doi.org/10.1117/1.3073891.

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Oliva, Diego, Erik Cuevas, Gonzalo Pajares, Daniel Zaldivar, and Valentín Osuna. "A Multilevel Thresholding algorithm using electromagnetism optimization." Neurocomputing 139 (September 2014): 357–81. http://dx.doi.org/10.1016/j.neucom.2014.02.020.

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Jui-Cheng Yen, Fu-Juay Chang, and Shyang Chang. "A new criterion for automatic multilevel thresholding." IEEE Transactions on Image Processing 4, no. 3 (1995): 370–78. http://dx.doi.org/10.1109/83.366472.

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Ouadfel, Salima, and Souham Meshoul. "Bio-inspired algorithms for multilevel image thresholding." International Journal of Computer Applications in Technology 49, no. 3/4 (2014): 207. http://dx.doi.org/10.1504/ijcat.2014.062358.

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Sathya, P. D., and R. Kayalvizhi. "Optimal multilevel thresholding using bacterial foraging algorithm." Expert Systems with Applications 38, no. 12 (2011): 15549–64. http://dx.doi.org/10.1016/j.eswa.2011.06.004.

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Yimit, Adiljan, and Yoshihiro Hagihara. "2D Direction Histogram-Based Rényi Entropic Multilevel Thresholding." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 3 (2018): 369–79. http://dx.doi.org/10.20965/jaciii.2018.p0369.

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2D histogram-based thresholding methods, in which the histogram is computed from local image features, have better performance than 1D histogram-based methods, but they take much more computation time. In this paper, we present a Rényi entropic multilevel thresholding (REMT) method based on a 2D direction histogram constructed from pixel values and local directional features. In addition to presenting a fast recursive method for REMT, we propose the Rényi entropic artificial bee colony multilevel thresholding (REABCMT) method to quickly find the optimal threshold values. In order to demonstrat

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Murinto, Murinto, Adhi Prahara, and Erik Iman Heri Ujianto. "Multilevel Thresholding Segmentasi Citra Warna Menggunakan Logarithmic Decreasing Inertia Weight Particle Swarm Optimization." Sainteks 19, no. 1 (2022): 13. http://dx.doi.org/10.30595/sainteks.v19i1.13295.

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Permasalahan utama dari segmentasi citra warna adalah tidak semua metode segmentasi citra yang ada saat ini dapat digunakan secara langsung seperti halnya pada citra gray scale. Maka dari itu diperlukan suatu teknik yang tepat untuk melakukan segmentasi warna. Teknik yang digunakan dalam penelitian ini adalah teknik segmentasi multilevel thresholding dengan menggunakan suatu bobot inersia logarithm decreasing particle swarm optimization (LogPSO). Bobot inersia Nilai threshold optimal diperoleh dengan cara memaksimalkan fungsi objektif Otsu. Teknik yang diusulkan mengurangi waktu perhitungan un

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Charansiriphaisan, Kanjana, Sirapat Chiewchanwattana, and Khamron Sunat. "A Comparative Study of Improved Artificial Bee Colony Algorithms Applied to Multilevel Image Thresholding." Mathematical Problems in Engineering 2013 (2013): 1–17. http://dx.doi.org/10.1155/2013/927591.

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Multilevel thresholding is a highly useful tool for the application of image segmentation. Otsu’s method, a common exhaustive search for finding optimal thresholds, involves a high computational cost. There has been a lot of recent research into various meta-heuristic searches in the area of optimization research. This paper analyses and discusses using a family of artificial bee colony algorithms, namely, the standard ABC, ABC/best/1, ABC/best/2, IABC/best/1, IABC/rand/1, and CABC, and some particle swarm optimization-based algorithms for searching multilevel thresholding. The strategy for an

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Al-Rahlawee, Anfal Thaer Hussein, and Javad Rahebi. "Multilevel thresholding of images with improved Otsu thresholding by black widow optimization algorithm." Multimedia Tools and Applications 80, no. 18 (2021): 28217–43. http://dx.doi.org/10.1007/s11042-021-10860-w.

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Ashir, Abubakar M. "Multilevel Thresholding for Image Segmentation Using Mean Gradient." Journal of Electrical and Computer Engineering 2022 (February 22, 2022): 1–9. http://dx.doi.org/10.1155/2022/1254852.

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Image binarization and segmentation have been one of the most important operations in digital image processing and related fields. In spite of the enormous number of research studies in this field over the years, huge challenges still exist hampering the usability of some existing algorithms. Some of these challenges include high computational cost, insufficient performance, lack of generalization and flexibility, lack of capacity to capture various image degradations, and many more. These challenges present difficulties in the choice of the algorithm to use, and sometimes, it is practically i

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T., Lakshmi Narayana, Md. Ayesha S., Mahesh Nayak B., and Rajeswari G. "DETECTION AND CLASSIFICATION OF BRAIN TUMOUR IN MRI IMAGES USING PARTICLE SWARM OPTIMIZATION AND MACHINE LEARNING ALGORITHM." International Journal of Engineering Sciences & Emerging Technologies 11, no. 2 (2023): 79–89. https://doi.org/10.5281/zenodo.10435294.

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<em>Today, people of all ages suffer from headaches and pain in the internal areas of the brain caused by a tumour. Radiologists face a difficult problem when it comes to the detection and categorization of tumours from magnetic resonance image modalities because this organ is critical to the decision-making process regarding the operation of the entire human body system, and any incorrect identification could seriously harm someone's health. Therefore, a multilevel thresholding-based particle swarm optimization and support vector machine classifier are presented here to decrease the false-fin

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Qin, Jun, ChuTing Wang, and GuiHe Qin. "A Multilevel Image Thresholding Method Based on Subspace Elimination Optimization." Mathematical Problems in Engineering 2019 (June 25, 2019): 1–11. http://dx.doi.org/10.1155/2019/6706590.

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Multilevel thresholding is to find the thresholds to segment the image with grey levels. Usually, the thresholds are so determined that some indicator functions of the segmented image are optimized. To improve the computational efficiency, we presented an optimization method for multilevel thresholding. First, the solution space is divided into subspaces. Second, the subspaces are searched to obtain their current local optimal value. Third, the subspaces that are of worse current optimal value are eliminated. Then, the next round of elimination is exerted in the remainder. The elimination is r

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Zhang, Jian, Huanzhou Li, Zhangguo Tang, Qiuping Lu, Xiuqing Zheng, and Jiliu Zhou. "An Improved Quantum-Inspired Genetic Algorithm for Image Multilevel Thresholding Segmentation." Mathematical Problems in Engineering 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/295402.

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A multilevel thresholding algorithm for histogram-based image segmentation is presented in this paper. The proposed algorithm introduces an adaptive adjustment strategy of the rotation angle and a cooperative learning strategy into quantum genetic algorithm (called IQGA). An adaptive adjustment strategy of the quantum rotation which is introduced in this study helps improving the convergence speed, search ability, and stability. Cooperative learning enhances the search ability in the high-dimensional solution space by splitting a high-dimensional vector into several one-dimensional vectors. Th

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Chakraborty, Falguni, Provas Kumar Roy, and Debashis Nandi. "Symbiotic Organisms Search Optimization for Multilevel Image Thresholding." International Journal of Swarm Intelligence Research 11, no. 2 (2020): 31–61. http://dx.doi.org/10.4018/ijsir.2020040103.

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Determination of optimum thresholds is the prime concern of any multilevel image thresholding technique. The traditional methods for multilevel thresholding are computationally expensive, time-consuming, and also suffer from lack of accuracy and stability. To address this issue, the authors propose a new methodology for multilevel image thresholding based on a recently developed meta-heuristic algorithm, Symbiotic Organisms Search (SOS). The SOS algorithm has been inspired by the symbiotic relationship among the organism in nature. This article has utilized the concept of the symbiotic relatio

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Charansiriphaisan, Kanjana, Sirapat Chiewchanwattana, and Khamron Sunat. "A Global Multilevel Thresholding Using Differential Evolution Approach." Mathematical Problems in Engineering 2014 (2014): 1–23. http://dx.doi.org/10.1155/2014/974024.

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Otsu’s function measures the properness of threshold values in multilevel image thresholding. Optimal threshold values are necessary for some applications and a global search algorithm is required. Differential evolution (DE) is an algorithm that has been used successfully for solving this problem. Because the difficulty of a problem grows exponentially when the number of thresholds increases, the ordinary DE fails when the number of thresholds is greater than 12. An improved DE, using a new mutation strategy, is proposed to overcome this problem. Experiments were conducted on 20 real images a

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