An efficient multi-thresholding based COVID-19 CT images segmentation approach using an improved equilibrium optimizer; Biomedical Signal Processing and Control; Vol. 73
| Parent link: | Biomedical Signal Processing and Control Vol. 73.— 2022.— [103401, 26 p.] |
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| Institution som forfatter: | |
| Andre forfattere: | , , , , , , |
| Summary: | Title screen Optimization is the process of searching for the optimal (best-so-far) solution among a wide range of solutions. Besides, in the last two decades, a family of algorithms known as metaheuristic algorithms (MHs) has been widely used. MHs have attracted researchers' interest due to their efficiency, easy implementation, and understanding. The equilibrium optimizer (EO) is a recent MH that has been used to tackle several real world problems. Despite the robustness of the EO algorithm, it suffers of the unbalance between the exploration and exploitation phases, this situation causes that the search process be trapped in local optimal values. In this study, an improved version of the EO that combines the standard operators with the dimension learning hunting (DLH) is introduced. The proposed method called I-EO is tested over the CEC'2020 benchmark functions. Quantitative and qualitative results confirmed the robustness and superiority of the proposed algorithm compared to a set of well-known optimization methods. Besides, I-EO is proposed to tackle a real-world application; the multi-level thresholding segmentation for a set of CT images of COVID-19 by maximizing the fuzzy entropy. The segmentation results show the excellent performance in all experiments and confirmed that the proposed I-EO could be an efficient tool for image segmentation. The different elements of the CT are properly segmented by the I-EO based approach. Moreover, the statistical analysis, quality metrics, comparisons and non-parametric tests validates the performance of the I-EO to segment CT images of COVID-19. Режим доступа: по договору с организацией-держателем ресурса |
| Sprog: | engelsk |
| Udgivet: |
2022
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| Fag: | |
| Online adgang: | https://doi.org/10.1016/j.bspc.2021.103401 |
| Format: | Electronisk Book Chapter |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=668645 |
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| 181 | 0 | |a i | |
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| 200 | 1 | |a An efficient multi-thresholding based COVID-19 CT images segmentation approach using an improved equilibrium optimizer |f H. H. Essam, E. Bahaa, D. A. Oliva Navarro [et al.] | |
| 203 | |a Text |c electronic | ||
| 300 | |a Title screen | ||
| 320 | |a [References: 80 tit.] | ||
| 330 | |a Optimization is the process of searching for the optimal (best-so-far) solution among a wide range of solutions. Besides, in the last two decades, a family of algorithms known as metaheuristic algorithms (MHs) has been widely used. MHs have attracted researchers' interest due to their efficiency, easy implementation, and understanding. The equilibrium optimizer (EO) is a recent MH that has been used to tackle several real world problems. Despite the robustness of the EO algorithm, it suffers of the unbalance between the exploration and exploitation phases, this situation causes that the search process be trapped in local optimal values. In this study, an improved version of the EO that combines the standard operators with the dimension learning hunting (DLH) is introduced. The proposed method called I-EO is tested over the CEC'2020 benchmark functions. Quantitative and qualitative results confirmed the robustness and superiority of the proposed algorithm compared to a set of well-known optimization methods. Besides, I-EO is proposed to tackle a real-world application; the multi-level thresholding segmentation for a set of CT images of COVID-19 by maximizing the fuzzy entropy. The segmentation results show the excellent performance in all experiments and confirmed that the proposed I-EO could be an efficient tool for image segmentation. The different elements of the CT are properly segmented by the I-EO based approach. Moreover, the statistical analysis, quality metrics, comparisons and non-parametric tests validates the performance of the I-EO to segment CT images of COVID-19. | ||
| 333 | |a Режим доступа: по договору с организацией-держателем ресурса | ||
| 461 | |t Biomedical Signal Processing and Control | ||
| 463 | |t Vol. 73 |v [103401, 26 p.] |d 2022 | ||
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a metaheuristics | |
| 610 | 1 | |a Equilibrium Optimizer (EO) | |
| 610 | 1 | |a Dimension learning hunting (DLH) | |
| 610 | 1 | |a multi-level thresholding | |
| 610 | 1 | |a image segmentation | |
| 610 | 1 | |a COVID-19 CT images | |
| 610 | 1 | |a метаэвристика | |
| 610 | 1 | |a оптимизаторы | |
| 610 | 1 | |a равновесие | |
| 610 | 1 | |a пороговые значения | |
| 610 | 1 | |a сегментация | |
| 610 | 1 | |a изображения | |
| 610 | 1 | |a снимки | |
| 610 | 1 | |a компьютерная томография | |
| 701 | 1 | |a Essam |b H. H. |g Houssein | |
| 701 | 1 | |a Bahaa |b E. |g El-din Helmy | |
| 701 | 1 | |a Oliva Navarro |b D. A. |c specialist in the field of informatics and computer technology |c Professor of Tomsk Polytechnic University |f 1983- |g Diego Alberto |3 (RuTPU)RU\TPU\pers\37366 | |
| 701 | 1 | |a Pradeep |b J. |g Jangir | |
| 701 | 1 | |a Premkumar |b M. | |
| 701 | 1 | |a Ahmed |b A. E. |g Elngar | |
| 701 | 1 | |a Hassan |b Sh. |g Shabana | |
| 712 | 0 | 2 | |a Национальный исследовательский Томский политехнический университет |b Инженерная школа информационных технологий и робототехники |c 2017- |x TPU |7 ca |8 rus |9 28330 |
| 801 | 0 | |a RU |b 63413507 |c 20230116 |g RCR | |
| 856 | 4 | |u https://doi.org/10.1016/j.bspc.2021.103401 | |
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