دورية أكاديمية
An efficient image segmentation method for skin cancer imaging using improved golden jackal optimization algorithm.
| العنوان: | An efficient image segmentation method for skin cancer imaging using improved golden jackal optimization algorithm. |
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| المؤلفون: | Houssein EH; Faculty of Computers and Information, Minia University, Minia, Egypt. Electronic address: essam.halim@mu.edu.eg., Abdelkareem DA; Faculty of Computers and Information, Luxor University, Luxor, Egypt. Electronic address: Doaa.Abdelkareem@fci.luxor.edu.eg., Emam MM; Faculty of Computers and Information, Minia University, Minia, Egypt. Electronic address: marwa.khalef@mu.edu.eg., Hameed MA; Faculty of Computers and Information, Luxor University, Luxor, Egypt. Electronic address: m.abdelhamid@fci.luxor.edu.eg., Younan M; Faculty of Computers and Information, Minia University, Minia, Egypt. Electronic address: mina.younan@mu.edu.eg. |
| المصدر: | Computers in biology and medicine [Comput Biol Med] 2022 Oct; Vol. 149, pp. 106075. Date of Electronic Publication: 2022 Sep 06. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Elsevier Country of Publication: United States NLM ID: 1250250 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-0534 (Electronic) Linking ISSN: 00104825 NLM ISO Abbreviation: Comput Biol Med Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: New York : Elsevier Original Publication: New York, Pergamon Press. |
| مواضيع طبية MeSH: | Jackals* , Skin Neoplasms*/diagnostic imaging, Algorithms ; Animals ; Diagnostic Imaging ; Humans ; Image Processing, Computer-Assisted/methods |
| مستخلص: | Skin cancer is one of the worst cancers nowadays that poses a severe threat to the health and safety of individuals. Therefore, skin cancer classification and early diagnosis are recommended to preserve human life. Multilevel thresholding image segmentation is well-known and influential technique for extracting regions of interest from skin cancer images to improve the classification process. Therefore, this paper proposes an efficient version of the recently developed golden jackal optimization (GJO) algorithm, the opposition-based golden jackal optimizer (IGJO). The IGJO algorithm is used to solve the multilevel thresholding problem using Otsu's method as an objective function. The proposed algorithm is compared with seven other meta-heuristic algorithms: whale optimization algorithm, seagull optimization algorithm, salp swarm algorithm, Harris hawks optimization, artificial gorilla troops optimizer, marine predators' algorithms, and original GJO algorithm. The performance of the proposed algorithm is evaluated using four popular performance measures: peak signal-to-noise ratio, structure similarity index, feature similarity index, and mean square error. Experimental results show that the proposed algorithm outperforms other alternative algorithms in terms of PSNR, SSIM, FSIM, and MSE segmentation metrics and effectively resolves the segmentation problem. Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (Copyright © 2022 Elsevier Ltd. All rights reserved.) |
| فهرسة مساهمة: | Keywords: Golden jackal optimization algorithm; Image processing; Meta-heuristic algorithms; Otsu’s method; Skin cancer |
| تواريخ الأحداث: | Date Created: 20220917 Date Completed: 20220923 Latest Revision: 20220930 |
| رمز التحديث: | 20240628 |
| DOI: | 10.1016/j.compbiomed.2022.106075 |
| PMID: | 36115303 |
| قاعدة البيانات: | MEDLINE |
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Full Text Finder | تدمد: | 1879-0534 |
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| DOI: | 10.1016/j.compbiomed.2022.106075 |