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Multilevel segmentation of 2D and volumetric medical images using hybrid Coronavirus Optimization Algorithm

Medical image segmentation is a crucial step in Computer-Aided Diagnosis systems, where accurate segmentation is vital for perfect disease diagnoses. This paper proposes a multilevel thresholding technique for 2D and 3D medical image segmentation using Otsu and Kapur's entropy methods as fitnes...

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Autores principales: Hosny, Khalid M., Khalid, Asmaa M., Hamza, Hanaa M., Mirjalili, Seyedali
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9398848/
https://www.ncbi.nlm.nih.gov/pubmed/36228462
http://dx.doi.org/10.1016/j.compbiomed.2022.106003
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author Hosny, Khalid M.
Khalid, Asmaa M.
Hamza, Hanaa M.
Mirjalili, Seyedali
author_facet Hosny, Khalid M.
Khalid, Asmaa M.
Hamza, Hanaa M.
Mirjalili, Seyedali
author_sort Hosny, Khalid M.
collection PubMed
description Medical image segmentation is a crucial step in Computer-Aided Diagnosis systems, where accurate segmentation is vital for perfect disease diagnoses. This paper proposes a multilevel thresholding technique for 2D and 3D medical image segmentation using Otsu and Kapur's entropy methods as fitness functions to determine the optimum threshold values. The proposed algorithm applies the hybridization concept between the recent Coronavirus Optimization Algorithm (COVIDOA) and Harris Hawks Optimization Algorithm (HHOA) to benefit from both algorithms' strengths and overcome their limitations. The improved performance of the proposed algorithm over COVIDOA and HHOA algorithms is demonstrated by solving 5 test problems from IEEE CEC 2019 benchmark problems. Medical image segmentation is tested using two groups of images, including 2D medical images and volumetric (3D) medical images, to demonstrate its superior performance. The utilized test images are from different modalities such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and X-ray images. The proposed algorithm is compared with seven well-known metaheuristic algorithms, where the performance is evaluated using four different metrics, including the best fitness values, Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Normalized Correlation Coefficient (NCC). The experimental results demonstrate the superior performance of the proposed algorithm in terms of convergence to the global optimum and making a good balance between exploration and exploitation properties. Moreover, the quality of the segmented images using the proposed algorithm at different threshold levels is better than the other methods according to PSNR, SSIM, and NCC values. Additionally, the Wilcoxon rank-sum test is conducted to prove the statistical significance of the proposed algorithm.
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spelling pubmed-93988482022-08-24 Multilevel segmentation of 2D and volumetric medical images using hybrid Coronavirus Optimization Algorithm Hosny, Khalid M. Khalid, Asmaa M. Hamza, Hanaa M. Mirjalili, Seyedali Comput Biol Med Article Medical image segmentation is a crucial step in Computer-Aided Diagnosis systems, where accurate segmentation is vital for perfect disease diagnoses. This paper proposes a multilevel thresholding technique for 2D and 3D medical image segmentation using Otsu and Kapur's entropy methods as fitness functions to determine the optimum threshold values. The proposed algorithm applies the hybridization concept between the recent Coronavirus Optimization Algorithm (COVIDOA) and Harris Hawks Optimization Algorithm (HHOA) to benefit from both algorithms' strengths and overcome their limitations. The improved performance of the proposed algorithm over COVIDOA and HHOA algorithms is demonstrated by solving 5 test problems from IEEE CEC 2019 benchmark problems. Medical image segmentation is tested using two groups of images, including 2D medical images and volumetric (3D) medical images, to demonstrate its superior performance. The utilized test images are from different modalities such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and X-ray images. The proposed algorithm is compared with seven well-known metaheuristic algorithms, where the performance is evaluated using four different metrics, including the best fitness values, Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Normalized Correlation Coefficient (NCC). The experimental results demonstrate the superior performance of the proposed algorithm in terms of convergence to the global optimum and making a good balance between exploration and exploitation properties. Moreover, the quality of the segmented images using the proposed algorithm at different threshold levels is better than the other methods according to PSNR, SSIM, and NCC values. Additionally, the Wilcoxon rank-sum test is conducted to prove the statistical significance of the proposed algorithm. Elsevier Ltd. 2022-11 2022-08-24 /pmc/articles/PMC9398848/ /pubmed/36228462 http://dx.doi.org/10.1016/j.compbiomed.2022.106003 Text en © 2022 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Hosny, Khalid M.
Khalid, Asmaa M.
Hamza, Hanaa M.
Mirjalili, Seyedali
Multilevel segmentation of 2D and volumetric medical images using hybrid Coronavirus Optimization Algorithm
title Multilevel segmentation of 2D and volumetric medical images using hybrid Coronavirus Optimization Algorithm
title_full Multilevel segmentation of 2D and volumetric medical images using hybrid Coronavirus Optimization Algorithm
title_fullStr Multilevel segmentation of 2D and volumetric medical images using hybrid Coronavirus Optimization Algorithm
title_full_unstemmed Multilevel segmentation of 2D and volumetric medical images using hybrid Coronavirus Optimization Algorithm
title_short Multilevel segmentation of 2D and volumetric medical images using hybrid Coronavirus Optimization Algorithm
title_sort multilevel segmentation of 2d and volumetric medical images using hybrid coronavirus optimization algorithm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9398848/
https://www.ncbi.nlm.nih.gov/pubmed/36228462
http://dx.doi.org/10.1016/j.compbiomed.2022.106003
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