| 000 | 04065nlm1a2200577 4500 | ||
|---|---|---|---|
| 001 | 668645 | ||
| 005 | 20231030042155.0 | ||
| 035 | _a(RuTPU)RU\TPU\network\39882 | ||
| 035 | _aRU\TPU\network\37272 | ||
| 090 | _a668645 | ||
| 100 | _a20230116a2022 k y0engy50 ba | ||
| 101 | 0 | _aeng | |
| 102 | _aNL | ||
| 135 | _adrcn ---uucaa | ||
| 181 | 0 | _ai | |
| 182 | 0 | _ab | |
| 200 | 1 |
_aAn efficient multi-thresholding based COVID-19 CT images segmentation approach using an improved equilibrium optimizer _fH. H. Essam, E. Bahaa, D. A. Oliva Navarro [et al.] |
|
| 203 |
_aText _celectronic |
||
| 300 | _aTitle screen | ||
| 320 | _a[References: 80 tit.] | ||
| 330 | _aOptimization 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 | _aРежим доступа: по договору с организацией-держателем ресурса | ||
| 461 | _tBiomedical Signal Processing and Control | ||
| 463 |
_tVol. 73 _v[103401, 26 p.] _d2022 |
||
| 610 | 1 | _aэлектронный ресурс | |
| 610 | 1 | _aтруды учёных ТПУ | |
| 610 | 1 | _ametaheuristics | |
| 610 | 1 | _aEquilibrium Optimizer (EO) | |
| 610 | 1 | _aDimension learning hunting (DLH) | |
| 610 | 1 | _amulti-level thresholding | |
| 610 | 1 | _aimage segmentation | |
| 610 | 1 | _aCOVID-19 CT images | |
| 610 | 1 | _aметаэвристика | |
| 610 | 1 | _aоптимизаторы | |
| 610 | 1 | _aравновесие | |
| 610 | 1 | _aпороговые значения | |
| 610 | 1 | _aсегментация | |
| 610 | 1 | _aизображения | |
| 610 | 1 | _aснимки | |
| 610 | 1 | _aкомпьютерная томография | |
| 701 | 1 |
_aEssam _bH. H. _gHoussein |
|
| 701 | 1 |
_aBahaa _bE. _gEl-din Helmy |
|
| 701 | 1 |
_aOliva Navarro _bD. A. _cspecialist in the field of informatics and computer technology _cProfessor of Tomsk Polytechnic University _f1983- _gDiego Alberto _2stltpush _3(RuTPU)RU\TPU\pers\37366 |
|
| 701 | 1 |
_aPradeep _bJ. _gJangir |
|
| 701 | 1 |
_aPremkumar _bM. |
|
| 701 | 1 |
_aAhmed _bA. E. _gElngar |
|
| 701 | 1 |
_aHassan _bSh. _gShabana |
|
| 712 | 0 | 2 |
_aНациональный исследовательский Томский политехнический университет _bИнженерная школа информационных технологий и робототехники _bОтделение информационных технологий _h7951 _2stltpush _3(RuTPU)RU\TPU\col\23515 |
| 801 | 2 |
_aRU _b63413507 _c20230116 _gRCR |
|
| 856 | 4 | _uhttps://doi.org/10.1016/j.bspc.2021.103401 | |
| 942 | _cCF | ||