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Houssein EH, Abdalkarim N, Hussain K, Mohamed E. Accurate multilevel thresholding image segmentation via oppositional Snake Optimization algorithm: Real cases with liver disease. Comput Biol Med 2024; 169:107922. [PMID: 38184861 DOI: 10.1016/j.compbiomed.2024.107922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 12/19/2023] [Accepted: 01/01/2024] [Indexed: 01/09/2024]
Abstract
Liver-related diseases significantly contribute to global mortality rates. Accurate segmentation of liver disease from CT scans is essential for early diagnosis and treatment selection, particularly in computer-aided diagnosis (CAD) systems. To address challenges posed by inconsistent liver presence and unclear boundaries, an enhanced Snake Optimization (SO) algorithm is proposed that integrates with opposition-based learning (OBL) called (SO-OBL), proving effective in global optimization and multilevel image segmentation. Experiments using CEC'2022 test functions compare SO-OBL with eleven recent and state-of-the-art metaheuristic algorithms, demonstrating its superior performance. Additionally, an advanced liver disease segmentation model based on SO-OBL incorporates an optimized multilevel thresholding technique, leveraging Otsu's function. Notable segmentation metric results, including FSIM = 0.947, SSIM = 0.941, PSNR = 24.876, MSE = 236.88, and execution time = 0.281, underscore the model's efficiency and potential for accurate diagnosis in CAD systems.
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Affiliation(s)
- Essam H Houssein
- Faculty of Computers and Information, Minia University, Minia, Egypt.
| | - Nada Abdalkarim
- Faculty of Computers and Information, Minia University, Minia, Egypt.
| | - Kashif Hussain
- Department of Science and Engineering, Solent University, Southampton, United Kingdom.
| | - Ebtsam Mohamed
- Faculty of Computers and Information, Minia University, Minia, Egypt.
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Kumar Sahoo S, Houssein EH, Premkumar M, Kumar Saha A, Emam MM. Self-adaptive moth flame optimizer combined with crossover operator and Fibonacci search strategy for COVID-19 CT image segmentation. Expert Syst Appl 2023; 227:120367. [PMID: 37193000 PMCID: PMC10163947 DOI: 10.1016/j.eswa.2023.120367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 04/15/2023] [Accepted: 05/01/2023] [Indexed: 05/18/2023]
Abstract
The COVID-19 is one of the most significant obstacles that humanity is now facing. The use of computed tomography (CT) images is one method that can be utilized to recognize COVID-19 in early stage. In this study, an upgraded variant of Moth flame optimization algorithm (Es-MFO) is presented by considering a nonlinear self-adaptive parameter and a mathematical principle based on the Fibonacci approach method to achieve a higher level of accuracy in the classification of COVID-19 CT images. The proposed Es-MFO algorithm is evaluated using nineteen different basic benchmark functions, thirty and fifty dimensional IEEE CEC'2017 test functions, and compared the proficiency with a variety of other fundamental optimization techniques as well as MFO variants. Moreover, the suggested Es-MFO algorithm's robustness and durability has been evaluated with tests including the Friedman rank test and the Wilcoxon rank test, as well as a convergence analysis and a diversity analysis. Furthermore, the proposed Es-MFO algorithm resolves three CEC2020 engineering design problems to examine the problem-solving ability of the proposed method. The proposed Es-MFO algorithm is then used to solve the COVID-19 CT image segmentation problem using multi-level thresholding with the help of Otsu's method. Comparison results of the suggested Es-MFO with basic and MFO variants proved the superiority of the newly developed algorithm.
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Affiliation(s)
- Saroj Kumar Sahoo
- Department of Mathematics, National Institute of Technology Agartala, Tripura 799046, India
| | - Essam H Houssein
- Faculty of Computers and Information, Minia University, Minia, Egypt
| | - M Premkumar
- Department of Electrical and Electronics Engineering, Dayananda Sagar College of Engineering, Bengaluru, Karnataka 560078, India
| | - Apu Kumar Saha
- Department of Mathematics, National Institute of Technology Agartala, Tripura 799046, India
| | - Marwa M Emam
- Faculty of Computers and Information, Minia University, Minia, Egypt
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3
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Das G, Swain M, Panda R, Naik MK, Agrawal S. A non-entropy-based optimal multilevel threshold selection technique for COVID-19 X-ray images using chance-based birds' intelligence. Soft comput 2023:1-21. [PMID: 37362283 PMCID: PMC10127190 DOI: 10.1007/s00500-023-08135-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/29/2023] [Indexed: 06/28/2023]
Abstract
Recently, image thresholding methods based on various entropy functions have been found popularity. Nonetheless, entropic-based methods depend on the spatial distribution of the grey level values in an image. Hence, the accuracy of these methods is limited due to the non-uniform distribution of the grey values. Further, the analysis of the COVID-19 X-ray images is evolved as an important area of research. Therefore, it is needed to develop an efficient method for the segmentation of the COVID-19 X-ray images. To address these issues, an efficient non-entropy-based thresholding method is suggested. A novel fitness function in terms of the segmentation score (SS) is introduced, which is used to reduce the segmentation error. A soft computing approach is suggested. An efficient optimizer using the chance-based birds' intelligence is introduced to maximize the fitness values. The new optimizer is validated utilizing the benchmark test functions. The statistical parameters reveal that the suggested optimizer is efficient. It shows a quite significant improvement over its counterparts-optimization based on seagull/cuckoo birds. Precisely, the paper includes three novel contributions-(i) fitness function, (ii) chance-based birds' intelligence for optimization, (iii) multiclass segmentation. The COVID-19 X-ray images are taken from the Kaggle Radiography database, to the experiment. Its results are compared with three different state-of-the-art entropy-based techniques-Tsallis, Kapur's, and Masi. For providing a statistical analysis, Friedman's mean rank test is conducted and our method Ranked one. Its superiority is claimed in terms of Peak Signal to Noise Ratio (PSNR), Feature Similarity Index (FSIM) and Structure Similarity Index (SSIM). On the whole, an improvement of about 11% in PSNR values is achieved using the proposed method. This method would be helpful for medical image analysis.
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Affiliation(s)
- Gyanesh Das
- Department of Electronics and TCE, Veer Surendra Sai University of Technology, Burla, Odisha 768018 India
| | - Monorama Swain
- Department of ECE, Silicon Institute of Technology, Bhubaneswar, Odisha 751024 India
| | - Rutuparna Panda
- Department of Electronics and TCE, Veer Surendra Sai University of Technology, Burla, Odisha 768018 India
| | - Manoj K. Naik
- Faculty of Engineering and Technology, Siksha O Anusandhan, Bhubaneswar, Odisha 751030 India
| | - Sanjay Agrawal
- Department of Electronics and TCE, Veer Surendra Sai University of Technology, Burla, Odisha 768018 India
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Ehsaeyan E. An efficient image segmentation method based on expectation maximization and Salp swarm algorithm. Multimed Tools Appl 2023:1-31. [PMID: 37362643 PMCID: PMC10061417 DOI: 10.1007/s11042-023-15149-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 11/14/2022] [Accepted: 03/13/2023] [Indexed: 06/28/2023]
Abstract
Multilevel image thresholding using Expectation Maximization (EM) is an efficient method for image segmentation. However, it has two weaknesses: 1) EM is a greedy algorithm and cannot jump out of local optima. 2) it cannot guarantee the number of required classes while estimating the histogram by Gaussian Mixture Models (GMM). in this paper, to overcome these shortages, a novel thresholding approach by combining EM and Salp Swarm Algorithm (SSA) is developed. SSA suggests potential points to the EM algorithm to fly to a better position. Moreover, a new mechanism is considered to maintain the number of desired clusters. Twenty-four medical test images are selected and examined by standard metrics such as PSNR and FSIM. The proposed method is compared with the traditional EM algorithm, and an average improvement of 5.27% in PSNR values and 2.01% in FSIM values were recorded. Also, the proposed approach is compared with four existing segmentation techniques by using CT scan images that Qatar University has collected. Experimental results depict that the proposed method obtains the first rank in terms of PSNR and the second rank in terms of FSIM. It has been observed that the proposed technique performs better performance in the segmentation result compared to other considered state-of-the-art methods.
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Affiliation(s)
- Ehsan Ehsaeyan
- Electrical Engineering Department, Sirjan University of Technology, Sirjan, Iran
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Ryalat MH, Dorgham O, Tedmori S, Al-Rahamneh Z, Al-Najdawi N, Mirjalili S. Harris hawks optimization for COVID-19 diagnosis based on multi-threshold image segmentation. Neural Comput Appl 2023; 35:6855-6873. [PMID: 36471798 PMCID: PMC9714421 DOI: 10.1007/s00521-022-08078-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Accepted: 11/22/2022] [Indexed: 12/04/2022]
Abstract
Digital image processing techniques and algorithms have become a great tool to support medical experts in identifying, studying, diagnosing certain diseases. Image segmentation methods are of the most widely used techniques in this area simplifying image representation and analysis. During the last few decades, many approaches have been proposed for image segmentation, among which multilevel thresholding methods have shown better results than most other methods. Traditional statistical approaches such as the Otsu and the Kapur methods are the standard benchmark algorithms for automatic image thresholding. Such algorithms provide optimal results, yet they suffer from high computational costs when multilevel thresholding is required, which is considered as an optimization matter. In this work, the Harris hawks optimization technique is combined with Otsu's method to effectively reduce the required computational cost while maintaining optimal outcomes. The proposed approach is tested on a publicly available imaging datasets, including chest images with clinical and genomic correlates, and represents a rural COVID-19-positive (COVID-19-AR) population. According to various performance measures, the proposed approach can achieve a substantial decrease in the computational cost and the time to converge while maintaining a level of quality highly competitive with the Otsu method for the same threshold values.
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Affiliation(s)
- Mohammad Hashem Ryalat
- grid.443749.90000 0004 0623 1491Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt, 19117 Jordan
| | - Osama Dorgham
- grid.443749.90000 0004 0623 1491Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt, 19117 Jordan ,grid.461585.b0000 0004 1762 8208School of Information Technology, Skyline University College, Sharjah, United Arab Emirates
| | - Sara Tedmori
- grid.29251.3d0000 0004 0404 9637King Hussein School of Computing Sciences, Princess Sumaya University for Technology, Amman, 11941 Jordan
| | - Zainab Al-Rahamneh
- grid.443749.90000 0004 0623 1491Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt, 19117 Jordan
| | - Nijad Al-Najdawi
- grid.443749.90000 0004 0623 1491Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt, 19117 Jordan
| | - Seyedali Mirjalili
- grid.449625.80000 0004 4654 2104Centre for Artificial Intelligence Research and Optimisation, Torrens University, Adelaide, SA 5000 Australia ,grid.15444.300000 0004 0470 5454Yonsei Frontier Lab, Yonsei University, Seoul, South Korea
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Jena B, Naik MK, Panda R, Abraham A. A novel minimum generalized cross entropy-based multilevel segmentation technique for the brain MRI/dermoscopic images. Comput Biol Med 2022; 151:106214. [PMID: 36308899 DOI: 10.1016/j.compbiomed.2022.106214] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 09/20/2022] [Accepted: 10/15/2022] [Indexed: 12/27/2022]
Abstract
BACKGROUND One of the challenging and the primary stages of medical image examination is the identification of the source of any disease, which may be the aberrant damage or change in tissue or organ caused by infections, injury, and a variety of other factors. Any such condition related to skin or brain sometimes advances in cancer and becomes a life-threatening disease. So, an efficient automatic image segmentation approach is required at the initial stage of medical image analysis. PURPOSE To make a segmentation process efficient and reliable, it is essential to use an appropriate objective function and an efficient optimization algorithm to produce optimal results. METHOD The above problem is resolved in this paper by introducing a new minimum generalized cross entropy (MGCE) as an objective function, with the inclusion of the degree of divergence. Another key contribution is the development of a new optimizer called opposition African vulture optimization algorithm (OAVOA). The proposed optimizer boosted the exploration, skill by inheriting the opposition-based learning. THE RESULTS The experimental work in this study starts with a performance evaluation of the optimizer over a set of standards (23 numbers) and IEEE CEC14 (8 numbers) Benchmark functions. The comparative analysis of test results shows that the OAVOA outperforms different state-of-the-art optimizers. The suggested OAVOA-MGCE based multilevel thresholding approach is carried out on two different types of medical images - Brain MRI Images (AANLIB dataset), and dermoscopic images (ISIC 2016 dataset) and found superior than other entropy-based thresholding methods.
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Affiliation(s)
- Bibekananda Jena
- Dept. of Electronics and Communication Engineering, Anil Neerukonda Institute of Technology & Science, Sangivalasa, Visakhapatnam, Andhra Pradesh, 531162, India.
| | - Manoj Kumar Naik
- Faculty of Engineering and Technology, Siksha O Anusandhan, Bhubaneswar, Odisha, 751030, India.
| | - Rutuparna Panda
- Dept of Electronics and Telecommunication Engineering, Veer Surendra Sai University of Technology, Burla, Odisha, 768018, India.
| | - Ajith Abraham
- Machine Intelligence Research Labs, Scientific Network for Innovation and Research Excellence, WA, 98071-2259, USA.
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Olmez Y, Sengur A, Koca GO, Rao RV. An adaptive multilevel thresholding method with chaotically-enhanced Rao algorithm. Multimed Tools Appl 2022; 82:12351-12377. [PMID: 36105661 PMCID: PMC9461387 DOI: 10.1007/s11042-022-13671-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 02/07/2022] [Accepted: 08/11/2022] [Indexed: 06/15/2023]
Abstract
Multilevel image thresholding is a well-known technique for image segmentation. Recently, various metaheuristic methods have been proposed for the determination of the thresholds for multilevel image segmentation. These methods are mainly based on metaphors and they have high complexity and their convergences are comparably slow. In this paper, a multilevel image thresholding approach is proposed that simplifies the thresholding problem by using a simple optimization technique instead of metaphor-based algorithms. More specifically, in this paper, Chaotic enhanced Rao (CER) algorithms are developed where eight chaotic maps namely Logistic, Sine, Sinusoidal, Gauss, Circle, Chebyshev, Singer, and Tent are used. Besides, in the developed CER algorithm, the number of thresholds is determined automatically, instead of manual determination. The performances of the developed CER algorithms are evaluated based on different statistical analysis metrics namely BDE, PRI, VOI, GCE, SSIM, FSIM, RMSE, PSNR, NK, AD, SC, MD, and NAE. The experimental works and the related evaluations are carried out on the BSDS300 dataset. The obtained experimental results demonstrate that the proposed CER algorithm outperforms the compared methods based on PRI, SSIM, FSIM, PSNR, RMSE, AD, and NAE metrics. In addition, the proposed method provides better convergence regarding speed and accuracy.
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Affiliation(s)
- Yagmur Olmez
- Department of Mechatronics Engineering, Faculty of Technology, University of Firat, 23119 Elazig, Turkey
| | - Abdulkadir Sengur
- Department of Electrical and Electronics Engineering, Faculty of Technology, University of Firat, 23119 Elazig, Turkey
| | - Gonca Ozmen Koca
- Department of Mechatronics Engineering, Faculty of Technology, University of Firat, 23119 Elazig, Turkey
| | - Ravipudi Venkata Rao
- Department of Mechanical Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat 395007 India
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Abualigah L, Al-Okbi NK, Elaziz MA, Houssein EH. Boosting Marine Predators Algorithm by Salp Swarm Algorithm for Multilevel Thresholding Image Segmentation. Multimed Tools Appl 2022; 81:16707-16742. [PMID: 35261554 PMCID: PMC8892122 DOI: 10.1007/s11042-022-12001-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 07/12/2021] [Accepted: 01/04/2022] [Indexed: 05/27/2023]
Abstract
Pixel rating is considered one of the commonly used critical factors in digital image processing that depends on intensity. It is used to determine the optimal image segmentation threshold. In recent years, the optimum threshold has been selected with great interest due to its many applications. Several methods have been used to find the optimum threshold, including the Otsu and Kapur methods. These methods are appropriate and easy to implement to define a single or bi-level threshold. However, when they are extended to multiple levels, they will cause some problems, such as long time-consuming, the high computational cost, and the needed improvement in their accuracy. To avoid these problems and determine the optimal multilevel image segmentation threshold, we proposed a hybrid Marine Predators Algorithm (MPA) with Salp Swarm Algorithm (SSA) to determine the optimal multilevel threshold image segmentation MPASSA. The obtained solutions of the proposed method are represented using the image histogram. Several standard evaluation measures, such as (the fitness function, time consumer, Peak Signal-to-Noise Ratio, Structural Similarity Index, etc.…) are employed to evaluate the proposed segmentation method's effectiveness. Several benchmark images are used to validate the proposed algorithm's performance (MPASSA). The results showed that the proposed MPASSA got better results than other well-known optimization algorithms published in the literature.
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Affiliation(s)
- Laith Abualigah
- Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, 11953 Jordan
- School of Computer Sciences, Universiti Sains Malaysia, 11800 Pulau Pinang, Malaysia
| | - Nada Khalil Al-Okbi
- Department of Computer Science, College of Science for Women, University of Baghdad, Baghdad, Iraq
| | - Mohamed Abd Elaziz
- Faculty of Computer Science & Engineering, Galala University, Suze, 435611 Egypt
- Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, 346 United Arab Emirates
- Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, 44519 Egypt
- School of Computer Science and Robotics, Tomsk Polytechnic University, Tomsk, 634050 Russia
| | - Essam H. Houssein
- Faculty of Computers and Information, Minia University, 61519 Minia, Egypt
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Chakraborty S, Saha AK, Nama S, Debnath S. COVID-19 X-ray image segmentation by modified whale optimization algorithm with population reduction. Comput Biol Med 2021; 139:104984. [PMID: 34739972 PMCID: PMC8556692 DOI: 10.1016/j.compbiomed.2021.104984] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 10/09/2021] [Accepted: 10/23/2021] [Indexed: 12/11/2022]
Abstract
Coronavirus disease 2019 (COVID-19) has caused a massive disaster in every human life field, including health, education, economics, and tourism, over the last year and a half. Rapid interpretation of COVID-19 patients' X-ray images is critical for diagnosis and, consequently, treatment of the disease. The major goal of this research is to develop a computational tool that can quickly and accurately determine the severity of an illness using COVID-19 chest X-ray pictures and improve the degree of diagnosis using a modified whale optimization method (WOA). To improve the WOA, a random initialization of the population is integrated during the global search phase. The parameters, coefficient vector (A) and constant value (b), are changed so that the algorithm can explore in the early stages while also exploiting the search space extensively in the latter stages. The efficiency of the proposed modified whale optimization algorithm with population reduction (mWOAPR) method is assessed by using it to segment six benchmark images using multilevel thresholding approach and Kapur's entropy-based fitness function calculated from the 2D histogram of greyscale images. By gathering three distinct COVID-19 chest X-ray images, the projected algorithm (mWOAPR) is utilized to segment the COVID-19 chest X-ray images. In both benchmark pictures and COVID-19 chest X-ray images, comparisons of the evaluated findings with basic and modified forms of metaheuristic algorithms supported the suggested mWOAPR's improved performance.
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Affiliation(s)
- Sanjoy Chakraborty
- Department of Computer Science and Engineering, National Institute of Technology, Agartala, Tripura, India; Department of Computer Science and Engineering, Iswar Chandra Vidyasagar College, Belonia, Tripura, India.
| | - Apu Kumar Saha
- Department of Mathematics, National Institute of Technology, Agartala, Tripura, India.
| | - Sukanta Nama
- Department of Applied Mathematics, Maharaja Bir Bikram University, Agartala, Tripura, India.
| | - Sudhan Debnath
- Department of Chemistry, Maharaja Bir Bikram College, Agartala, Tripura, India.
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Abd Elaziz M, Nabil N, Moghdani R, Ewees AA, Cuevas E, Lu S. Multilevel thresholding image segmentation based on improved volleyball premier league algorithm using whale optimization algorithm. Multimed Tools Appl 2021; 80:12435-12468. [PMID: 33456315 PMCID: PMC7797715 DOI: 10.1007/s11042-020-10313-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 08/26/2020] [Accepted: 12/22/2020] [Indexed: 05/31/2023]
Abstract
Multilevel thresholding image segmentation has received considerable attention in several image processing applications. However, the process of determining the optimal threshold values (as the preprocessing step) is time-consuming when traditional methods are used. Although these limitations can be addressed by applying metaheuristic methods, such approaches may be idle with a local solution. This study proposed an alternative multilevel thresholding image segmentation method called VPLWOA, which is an improved version of the volleyball premier league (VPL) algorithm using the whale optimization algorithm (WOA). In VPLWOA, the WOA is used as a local search system to improve the learning phase of the VPL algorithm. A set of experimental series is performed using two different image datasets to assess the performance of the VPLWOA in determining the values that may be optimal threshold, and the performance of this algorithm is compared with other approaches. Experimental results show that the proposed VPLWOA outperforms the other approaches in terms of several performance measures, such as signal-to-noise ratio and structural similarity index.
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Affiliation(s)
- Mohamed Abd Elaziz
- Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt
| | - Neggaz Nabil
- Faculté des mathématiques et informatique - Département d’Informatique- Laboratoire SIMPA, Université des Sciences et de la Technologie d’Oran Mohammed Boudiaf, USTO-MB, BP 1505, El M’naouer, 31000 Oran, Algeria
| | - Reza Moghdani
- Industrial Management Department, Persian Gulf University, Boushehr, Iran
| | - Ahmed A. Ewees
- Department of Computer, Damietta University, Damietta, Egypt
| | - Erik Cuevas
- Departamento de Electrónica, Universidad de Guadalajara, CUCEI Av. Revolución 1500, 44430 Guadalajara, Mexico
| | - Songfeng Lu
- School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074 China
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Goez MM, Torres-Madronero MC, Rothlisberger S, Delgado-Trejos E. Joint pre-processing framework for two-dimensional gel electrophoresis images based on nonlinear filtering, background correction and normalization techniques. BMC Bioinformatics 2020; 21:376. [PMID: 32867673 PMCID: PMC7457503 DOI: 10.1186/s12859-020-03713-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Accepted: 08/18/2020] [Indexed: 12/01/2022] Open
Abstract
BACKGROUND Two-dimensional gel electrophoresis (2-DGE) is a commonly used tool for proteomic analysis. This gel-based technique separates proteins in a sample according to their isoelectric point and molecular weight. 2-DGE images often present anomalies due to the acquisition process, such as: diffuse and overlapping spots, and background noise. This study proposes a joint pre-processing framework that combines the capabilities of nonlinear filtering, background correction and image normalization techniques for pre-processing 2-DGE images. Among the most important, joint nonlinear diffusion filtering, adaptive piecewise histogram equalization and multilevel thresholding were evaluated using both synthetic data and real 2-DGE images. RESULTS An improvement of up to 46% in spot detection efficiency was achieved for synthetic data using the proposed framework compared to implementing a single technique of either normalization, background correction or filtering. Additionally, the proposed framework increased the detection of low abundance spots by 20% for synthetic data compared to a normalization technique, and increased the background estimation by 67% compared to a background correction technique. In terms of real data, the joint pre-processing framework reduced the false positives up to 93%. CONCLUSIONS The proposed joint pre-processing framework outperforms results achieved with a single approach. The best structure was obtained with the ordered combination of adaptive piecewise histogram equalization for image normalization, geometric nonlinear diffusion (GNDF) for filtering, and multilevel thresholding for background correction.
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Affiliation(s)
- Manuel Mauricio Goez
- Smart Machine and Pattern Recognition Laboratory - MIRP, Faculty of Engineering, Instituto Tecnologico Metropolitano ITM, Medellin, 050012 Colombia
| | - Maria C. Torres-Madronero
- Smart Machine and Pattern Recognition Laboratory - MIRP, Faculty of Engineering, Instituto Tecnologico Metropolitano ITM, Medellin, 050012 Colombia
| | - Sarah Rothlisberger
- Biomedical Innovation and Research Group, Faculty of Applied and Exact Sciences, Instituto Tecnologico Metropolitano ITM, Medellin, 050034 Colombia
| | - Edilson Delgado-Trejos
- AMYSOD Lab (Parque i), CM&P Research Group, Quality and Production Department, Instituto Tecnologico Metropolitano ITM, Medellin, 050034 Colombia
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Avuti SK, Bajaj V, Kumar A, Singh GK. A novel pectoral muscle segmentation from scanned mammograms using EMO algorithm. Biomed Eng Lett 2019; 9:481-96. [PMID: 31799016 DOI: 10.1007/s13534-019-00135-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2019] [Revised: 09/06/2019] [Accepted: 10/20/2019] [Indexed: 10/25/2022] Open
Abstract
Mammogram images are majorly used for detecting the breast cancer. The level of positivity of breast cancer is detected after excluding the pectoral muscle from mammogram images. Hence, it is very significant to identify and segment the pectoral muscle from the mammographic images. In this work, a new multilevel thresholding, on the basis of electro-magnetism optimization (EMO) technique, is proposed. The EMO works on the principle of attractive and repulsive forces among the charges to develop the members of a population. Here, both Kapur's and Otsu based cost functions are employed with EMO separately. These standard functions are executed over the EMO operator till the best solution is achieved. Thus, optimal threshold levels can be identified for the considered mammographic image. The proposed methodology is applied on all the three twenty-two mammogram images available in mammographic image analysis society dataset, and successful segmentation of the pectoral muscle is achieved for majority of the mammogram images. Hence, the proposed algorithm is found to be robust for variations in the pectoral muscle.
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Abstract
Image segmentation is considered as one of the most fundamental tasks in image processing applications. Segmentation of magnetic resonance (MR) brain images is also an important pre-processing step, since many neural disorders are associated with brain's volume changes. As a result, brain image segmentation can be considered as an essential measure toward automated diagnosis or interpretation of regions of interest, which can help surgical planning, analyzing changes of brain's volume in different tissue types, and identifying neural disorders. In many neural disorders such as Alzheimer and epilepsy, determining the volume of different brain tissues (i.e., white matter, gray matter, and cerebrospinal fluids) has been proven to be effective in quantifying diseases. A traditional way for segmenting brain images involves the use of a medical expert's experience in manually determining the boundary of different regions of interest in brain images. It may seem that manual segmentation of MR brain images by an expert is the first and the best choice. However, this method is proved to be time-consuming and challenging. Hence, numerous MR brain image segmentation methods with different degrees of complexity and accuracy have been introduced recently. Our work proposes an optimized thresholding method for segmentation of MR brain images using biologically inspired ant colony algorithm. In this proposed algorithm, textural features are adopted as heuristic information. Besides, post-processing image enhancement based on homogeneity is also performed to achieve a better performance. The empirical results on axial T1-weighted MR brain images have demonstrated competitive accuracy to traditional meta-heuristic methods, K-means, and expectation maximization.
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