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Xu M, Cao L, Lu D, Hu Z, Yue Y. Application of Swarm Intelligence Optimization Algorithms in Image Processing: A Comprehensive Review of Analysis, Synthesis, and Optimization. Biomimetics (Basel) 2023; 8:235. [PMID: 37366829 DOI: 10.3390/biomimetics8020235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 05/27/2023] [Accepted: 06/01/2023] [Indexed: 06/28/2023] Open
Abstract
Image processing technology has always been a hot and difficult topic in the field of artificial intelligence. With the rise and development of machine learning and deep learning methods, swarm intelligence algorithms have become a hot research direction, and combining image processing technology with swarm intelligence algorithms has become a new and effective improvement method. Swarm intelligence algorithm refers to an intelligent computing method formed by simulating the evolutionary laws, behavior characteristics, and thinking patterns of insects, birds, natural phenomena, and other biological populations. It has efficient and parallel global optimization capabilities and strong optimization performance. In this paper, the ant colony algorithm, particle swarm optimization algorithm, sparrow search algorithm, bat algorithm, thimble colony algorithm, and other swarm intelligent optimization algorithms are deeply studied. The model, features, improvement strategies, and application fields of the algorithm in image processing, such as image segmentation, image matching, image classification, image feature extraction, and image edge detection, are comprehensively reviewed. The theoretical research, improvement strategies, and application research of image processing are comprehensively analyzed and compared. Combined with the current literature, the improvement methods of the above algorithms and the comprehensive improvement and application of image processing technology are analyzed and summarized. The representative algorithms of the swarm intelligence algorithm combined with image segmentation technology are extracted for list analysis and summary. Then, the unified framework, common characteristics, different differences of the swarm intelligence algorithm are summarized, existing problems are raised, and finally, the future trend is projected.
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Affiliation(s)
- Minghai Xu
- School of Intelligent Manufacturing and Electronic Engineering, Wenzhou University of Technology, Wenzhou 325035, China
| | - Li Cao
- School of Intelligent Manufacturing and Electronic Engineering, Wenzhou University of Technology, Wenzhou 325035, China
| | - Dongwan Lu
- Intelligent Information Systems Institute, Wenzhou University, Wenzhou 325035, China
| | - Zhongyi Hu
- Intelligent Information Systems Institute, Wenzhou University, Wenzhou 325035, China
| | - Yinggao Yue
- School of Intelligent Manufacturing and Electronic Engineering, Wenzhou University of Technology, Wenzhou 325035, China
- Intelligent Information Systems Institute, Wenzhou University, Wenzhou 325035, China
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Li M, Li P, Liu Y. IDEFE algorithm: IDE algorithm optimizes the fuzzy entropy for the gland segmentation. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:4896-4911. [PMID: 36896528 DOI: 10.3934/mbe.2023227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Breast cancer occurs in the epithelial tissue of the gland, so the accuracy of gland segmentation is crucial to the physician's diagnosis. An innovative technique for breast mammography image gland segmentation is put forth in this paper. In the first step, the algorithm designed the gland segmentation evaluation function. Then a new mutation strategy is established, and the adaptive controlled variables are used to balance the ability of improved differential evolution (IDE) in terms of investigation and convergence. To evaluate its performance, The proposed method is validated on a number of benchmark breast images, including four types of glands from the Quanzhou First Hospital, Fujian, China. Furthermore, the proposed algorithm is been systematically compared to five state-of-the-art algorithms. From the average MSSIM and boxplot, the evidence suggests that the mutation strategy may be effective in searching the topography of the segmented gland problem. The experiment results demonstrated that the proposed method has the best gland segmentation results compared to other algorithms.
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Affiliation(s)
- Mingzhu Li
- Department of Thyroid and Breast Surgery, East Branch of Quanzhou First Hospital, Fujian 362000, China
| | - Ping Li
- Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, Fujian 362000, China
| | - Yao Liu
- College of Medicine, Huaqiao University, Quanzhou, Fujian 362021, China
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Shehab M, Abu-Hashem MA, Shambour MKY, Alsalibi AI, Alomari OA, Gupta JND, Alsoud AR, Abuhaija B, Abualigah L. A Comprehensive Review of Bat Inspired Algorithm: Variants, Applications, and Hybridization. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2022; 30:765-797. [PMID: 36157973 PMCID: PMC9490733 DOI: 10.1007/s11831-022-09817-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 08/26/2022] [Indexed: 06/16/2023]
Abstract
Bat algorithm (BA) is one of the promising metaheuristic algorithms. It proved its efficiency in dealing with various optimization problems in diverse fields, such as power and energy systems, economic load dispatch problems, engineering design, image processing and medical applications. Thus, this review introduces a comprehensive and exhaustive review of the BA, as well as evaluates its main characteristics by comparing it with other optimization algorithms. The review paper highlights the performance of BA in different applications and the modifications that have been conducted by researchers (i.e., variants of BA). At the end, the conclusions focus on the current work on BA, highlighting its weaknesses, and suggest possible future research directions. The review paper will be helpful for the researchers and practitioners of BA belonging to a wide range of audiences from the domains of optimization, engineering, medical, data mining and clustering. As well, it is wealthy in research on health, environment and public safety. Also, it will aid those who are interested by providing them with potential future research.
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Affiliation(s)
- Mohammad Shehab
- Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, 11953 Jordan
| | - Muhannad A. Abu-Hashem
- Department of Geomatics, Faculty of Architecture and Planning, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Mohd Khaled Yousef Shambour
- The Custodian of the Two Holy Mosques Institute for Hajj and Umrah Research, Umm Al-Qura University, Mecca, Saudi Arabia
| | - Ahmed Izzat Alsalibi
- Department of Information Technology, Faculty of Engineering and Information Technology, Israa University, Gaza, Palestine
| | | | | | - Anas Ratib Alsoud
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328 Jordan
| | - Belal Abuhaija
- Department of Computer Science, Wenzhou-Kean University, Wenzhou, China
| | - Laith Abualigah
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328 Jordan
- Faculty of Information Technology, Middle East University, Amman, 11831 Jordan
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A novel multilevel color image segmentation technique based on an improved firefly algorithm and energy curve. EVOLVING SYSTEMS 2022. [DOI: 10.1007/s12530-022-09460-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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Mousavirad SJ, Oliva D, Chakrabortty RK, Zabihzadeh D, Hinojosa S. Population-based self-adaptive Generalised Masi Entropy for image segmentation: A novel representation. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108610] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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Qi H, Zhang G, Jia H, Xing Z. A hybrid equilibrium optimizer algorithm for multi-level image segmentation. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:4648-4678. [PMID: 34198458 DOI: 10.3934/mbe.2021236] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Threshlod image segmentation is a classic method of color image segmentation. In this paper, we propose a hybrid equilibrium optimizer algorithm for multi-level image segmentation. When multi-level threshold method calculates the neighborhood mean and median of color image, it takes huge challenge to find the optimal threshold. We use the proposed method to optimize the multi-level threshold method and get the optimal threshold from the color image. In order to test the performance of the proposed method, we select the CEC2015 dataset as the benchmark function. The result shows that the proposed method improves the optimization ability of the original algorithm. And then, the classic images and wood fiber images are taken as experimental objects to analyze the segmentation result. The experimental results show that the proposed method has a good performance in Uniformity measure, Peak Signal-to-Noise Ratio and Feature Similarity Index and CPU time.
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Affiliation(s)
- Hong Qi
- School of Information and Computer Engineering, Northeast Forestry University, China
| | - Guanglei Zhang
- School of Information and Computer Engineering, Northeast Forestry University, China
| | - Heming Jia
- School of Information Engineering, Sanming Universiy, China
| | - Zhikai Xing
- School of Electrical Engineering and Automation, Wuhan University, China
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Taramasco C, Olivares R, Munoz R, Soto R, Villar M, de Albuquerque VHC. The patient bed assignment problem solved by autonomous bat algorithm. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2019.105484] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Bhandari AK, Rahul K. A novel local contrast fusion-based fuzzy model for color image multilevel thresholding using grasshopper optimization. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2019.105515] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Chaudhari A, Kulkarni J. Cerebral edema segmentation using textural feature. Biocybern Biomed Eng 2019. [DOI: 10.1016/j.bbe.2019.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Dragonfly Algorithm with Opposition-Based Learning for Multilevel Thresholding Color Image Segmentation. Symmetry (Basel) 2019. [DOI: 10.3390/sym11050716] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Multilevel thresholding is a very active research field in image segmentation, and has been successfully used in various applications. However, the computational time will increase exponentially as the number of thresholds increases, and for color images which contain more information this is even worse. To overcome the drawback while maintaining segmentation accuracy, a modified version of dragonfly algorithm (DA) with opposition-based learning (OBLDA) for color image segmentation is proposed in this paper. The opposition-based learning (OBL) strategy simultaneously considers the current solution and the opposite solution, which are symmetrical in the search space. With the introduction of OBL, the proposed algorithm has a faster convergence speed and more balanced exploration–exploitation compared with the original DA. In order to clearly demonstrate the outstanding performance of the OBLDA, the proposed method is compared with seven state-of-the-art meta-heuristic algorithms, through experiments on 10 test images. The optimal threshold values are calculated by the maximization of between-class variance and Kapur’s entropy. Meanwhile, some indicators, including peak signal to noise ratio (PSNR), feature similarity index (FSIM), structure similarity index (SSIM), the average fitness values, standard deviation (STD), and computation time are used as evaluation criteria in the experiments. The promising results reveal that proposed method has the advantages of high accuracy and remarkable stability. Wilcoxon’s rank sum test and Friedman test are also performed to verify the superiority of OBLDA in a statistical way. Furthermore, various satellite images are also included for robustness testing. In conclusion, the OBLDA algorithm is a feasible and effective method for multilevel thresholding color image segmentation.
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Song S, Jia H, Ma J. A Chaotic Electromagnetic Field Optimization Algorithm Based on Fuzzy Entropy for Multilevel Thresholding Color Image Segmentation. ENTROPY (BASEL, SWITZERLAND) 2019; 21:E398. [PMID: 33267113 PMCID: PMC7514892 DOI: 10.3390/e21040398] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Revised: 04/11/2019] [Accepted: 04/12/2019] [Indexed: 11/21/2022]
Abstract
Multilevel thresholding segmentation of color images is an important technology in various applications which has received more attention in recent years. The process of determining the optimal threshold values in the case of traditional methods is time-consuming. In order to mitigate the above problem, meta-heuristic algorithms have been employed in this field for searching the optima during the past few years. In this paper, an effective technique of Electromagnetic Field Optimization (EFO) algorithm based on a fuzzy entropy criterion is proposed, and in addition, a novel chaotic strategy is embedded into EFO to develop a new algorithm named CEFO. To evaluate the robustness of the proposed algorithm, other competitive algorithms such as Artificial Bee Colony (ABC), Bat Algorithm (BA), Wind Driven Optimization (WDO), and Bird Swarm Algorithm (BSA) are compared using fuzzy entropy as the fitness function. Furthermore, the proposed segmentation method is also compared with the most widely used approaches of Otsu's variance and Kapur's entropy to verify its segmentation accuracy and efficiency. Experiments are conducted on ten Berkeley benchmark images and the simulation results are presented in terms of peak signal to noise ratio (PSNR), mean structural similarity (MSSIM), feature similarity (FSIM), and computational time (CPU Time) at different threshold levels of 4, 6, 8, and 10 for each test image. A series of experiments can significantly demonstrate the superior performance of the proposed technique, which can deal with multilevel thresholding color image segmentation excellently.
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Affiliation(s)
| | - Heming Jia
- College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China
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Kapur's Entropy for Color Image Segmentation Based on a Hybrid Whale Optimization Algorithm. ENTROPY 2019; 21:e21030318. [PMID: 33267032 PMCID: PMC7514802 DOI: 10.3390/e21030318] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Accepted: 03/21/2019] [Indexed: 11/17/2022]
Abstract
In this paper, a new hybrid whale optimization algorithm (WOA) called WOA-DE is proposed to better balance the exploitation and exploration phases of optimization. Differential evolution (DE) is adopted as a local search strategy with the purpose of enhancing exploitation capability. The WOA-DE algorithm is then utilized to solve the problem of multilevel color image segmentation that can be considered as a challenging optimization task. Kapur's entropy is used to obtain an efficient image segmentation method. In order to evaluate the performance of proposed algorithm, different images are selected for experiments, including natural images, satellite images and magnetic resonance (MR) images. The experimental results are compared with state-of-the-art meta-heuristic algorithms as well as conventional approaches. Several performance measures have been used such as average fitness values, standard deviation (STD), peak signal to noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), Wilcoxon's rank sum test, and Friedman test. The experimental results indicate that the WOA-DE algorithm is superior to the other meta-heuristic algorithms. In addition, to show the effectiveness of the proposed technique, the Otsu method is used for comparison.
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Jia H, Ma J, Song W. Multilevel Thresholding Segmentation for Color Image Using Modified Moth-Flame Optimization. IEEE ACCESS 2019. [PMID: 0 DOI: 10.1109/access.2019.2908718] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
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Pare S, Bhandari AK, Kumar A, Singh GK. Rényi’s Entropy and Bat Algorithm Based Color Image Multilevel Thresholding. ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING 2019. [DOI: 10.1007/978-981-13-0923-6_7] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
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Oliva D, Abd Elaziz M, Hinojosa S. Multilevel Thresholding for Image Segmentation Based on Metaheuristic Algorithms. METAHEURISTIC ALGORITHMS FOR IMAGE SEGMENTATION: THEORY AND APPLICATIONS 2019:59-69. [DOI: 10.1007/978-3-030-12931-6_6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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Segato Dos Santos LF, Neves LA, Rozendo GB, Ribeiro MG, Zanchetta do Nascimento M, Azevedo Tosta TA. Multidimensional and fuzzy sample entropy (SampEn MF) for quantifying H&E histological images of colorectal cancer. Comput Biol Med 2018; 103:148-160. [PMID: 30368171 DOI: 10.1016/j.compbiomed.2018.10.013] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Revised: 09/22/2018] [Accepted: 10/13/2018] [Indexed: 12/23/2022]
Abstract
In this study, we propose to use a method based on the combination of sample entropy with multiscale and multidimensional approaches, along with a fuzzy function. The model was applied to quantify and classify H&E histological images of colorectal cancer. The multiscale approach was defined by analysing windows of different sizes and variations in tolerance for determining pattern similarity. The multidimensional strategy was performed by considering each pixel in the colour image as an n-dimensional vector, which was analysed from the Minkowski distance. The fuzzy strategy was a Gaussian function used to verify the pertinence of the distances between windows. The result was a method capable of computing similarities between pixels contained in windows of various sizes, as well as the information present in the colour channels. The power of quantification was tested in a public colorectal image dataset, which was composed of both benign and malignant classes. The results were given as inputs for classifiers of different categories and analysed by applying the k-fold cross-validation and holdout methods. The derived performances indicate that the proposed association was capable of distinguishing the benign and malignant groups, with values that surpassed those results obtained with important techniques available in the Literature. The best performance was an AUC value of 0.983, an important result, mainly when we consider the difficulties of clinical practice for the diagnosis of the colorectal cancer.
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Affiliation(s)
- Luiz Fernando Segato Dos Santos
- Department of Computer Science and Statistics (DCCE), São Paulo State University (UNESP), Rua Cristóvão Colombo, 2265, 15054-000, São José do Rio Preto, São Paulo, Brazil.
| | - Leandro Alves Neves
- Department of Computer Science and Statistics (DCCE), São Paulo State University (UNESP), Rua Cristóvão Colombo, 2265, 15054-000, São José do Rio Preto, São Paulo, Brazil.
| | - Guilherme Botazzo Rozendo
- Department of Computer Science and Statistics (DCCE), São Paulo State University (UNESP), Rua Cristóvão Colombo, 2265, 15054-000, São José do Rio Preto, São Paulo, Brazil.
| | - Matheus Gonçalves Ribeiro
- Department of Computer Science and Statistics (DCCE), São Paulo State University (UNESP), Rua Cristóvão Colombo, 2265, 15054-000, São José do Rio Preto, São Paulo, Brazil.
| | - Marcelo Zanchetta do Nascimento
- Faculty of Computation (FACOM), Federal University of Uberlândia (UFU), Avenida João Neves de Ávila 2121, Bl.B, 38400-902, Uberlândia, Minas Gerais, Brazil.
| | - Thaína Aparecida Azevedo Tosta
- Center of Mathematics, Computing and Cognition, Federal University of ABC (UFABC), Avenida dos Estados, 5001, 09210-580, Santo André, São Paulo, Brazil.
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Bhandari AK. A novel beta differential evolution algorithm-based fast multilevel thresholding for color image segmentation. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3771-z] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Maolood IY, Al-Salhi YEA, Lu S. Thresholding for Medical Image Segmentation for Cancer using Fuzzy Entropy with Level Set Algorithm. Open Med (Wars) 2018; 13:374-383. [PMID: 30211320 PMCID: PMC6132127 DOI: 10.1515/med-2018-0056] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2018] [Accepted: 08/07/2018] [Indexed: 11/15/2022] Open
Abstract
In this study, an effective means for detecting cancer region through different types of medical image segmentation are presented and explained. We proposed a new method for cancer segmentation on the basis of fuzzy entropy with a level set (FELs) thresholding. The proposed method was successfully utilized to segment cancer images and then efficiently performed the segmentation of test ultrasound image, brain MRI, and dermoscopy image compared with algorithms proposed in previous studies. Results showed an excellent performance of the proposed method in detecting cancer image segmentation in terms of accuracy, precision, specificity, and sensitivity measures.
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Affiliation(s)
- Ismail Yaqub Maolood
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | | | - Songfeng Lu
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.,Shenzhen Huazhong University of Science and Technology Research Institute, Shenzhen, 518063, China
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Fuzzy entropy based on differential evolution for breast gland segmentation. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2018; 41:1101-1114. [PMID: 30203178 DOI: 10.1007/s13246-018-0672-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2018] [Accepted: 08/09/2018] [Indexed: 10/28/2022]
Abstract
For the diagnosis and treatment of breast tumors, the automatic detection of glands is a crucial step. The true segmentation of the gland is directly related to effective treatment effect of the patient. Therefore, it is necessary to propose an automatic segmentation algorithm based on mammary gland features. A segmentation method of differential evolution (DE) fuzzy entropy based on mammary gland is proposed in the paper. According to the image fuzzy entropy, the evaluation function of image segmentation is constructed in the first step. Then, the method adopts DE, the image fuzzy entropy parameter is regard as the initial population of individual. After the mutation, crossover and selection of three evolutionary processes to search for the maximum fuzzy entropy of parameters, the optimal threshold of the segmented gland is achieved. Finally, the mammary gland is segmented by the threshold method of maximum fuzzy entropy. Eight breast images with four tissue types are tested 100 times, with accuracy (Acc), sensitivity (Sen), specificity (Spe), positive predictive value (PPV), negative predicted value (NPV), and average structural similarity (Mssim) to measure the segmentation result. The Acc of the proposed algorithm is 98.46 ± 8.02E-03%, 95.93 ± 2.38E-02%, 93.88 ± 6.59E-02%, 94.73 ± 1.82E-01%, 96.19 ± 1.15E-02%, and 97.51 ± 1.36E-02%, 96.64 ± 6.35E-02%, and 94.76 ± 6.21E-02%, respectively. The mean Mssim values of the 100 tests were 0.985, 0.933, 0.924, 0.907, 0.984, 0.928, 0.938, and 0.941, respectively. Our proposed algorithm is more effective and robust in comparison to the other fuzzy entropy based on swarm intelligent optimization algorithms. The experimental results show that the proposed algorithm has higher accuracy in the segmentation of mammary glands, and may serve as a gold standard in the analysis of treatment of breast tumors.
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Jayabarathi T, Raghunathan T, Gandomi AH. The Bat Algorithm, Variants and Some Practical Engineering Applications: A Review. NATURE-INSPIRED ALGORITHMS AND APPLIED OPTIMIZATION 2018. [DOI: 10.1007/978-3-319-67669-2_14] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
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Sarkar S, Das S, Chaudhuri SS. Multi-level thresholding with a decomposition-based multi-objective evolutionary algorithm for segmenting natural and medical images. Appl Soft Comput 2017. [DOI: 10.1016/j.asoc.2016.10.032] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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