An Improved Marine Predators Algorithm With Fuzzy Entropy for Multi-Level Thresholding: Real World Example of COVID-19 CT Image Segmentation

Medical imaging techniques play a critical role in diagnosing diseases and patient healthcare. They help in treatment, diagnosis, and early detection. Image segmentation is one of the most important steps in processing medical images, and it has been widely used in many applications. Multi-level thr...

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Autores principales:	Mohamed Abd Elaziz, Ahmed A. Ewees, Dalia Yousri, Husein S. Naji Alwerfali, Qamar A. Awad, Songfeng Lu, Mohammed A. A. Al-Qaness
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2020
Materias:	Image segmentation multi-level thresholding moth-?ame optimization (MFO) marine predators algorithm (MPA) COVID-19 swarm intelligence Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/f53d8e74494d480fa64cbf9572ce1503
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spelling	oai:doaj.org-article:f53d8e74494d480fa64cbf9572ce15032021-11-19T00:03:56ZAn Improved Marine Predators Algorithm With Fuzzy Entropy for Multi-Level Thresholding: Real World Example of COVID-19 CT Image Segmentation2169-353610.1109/ACCESS.2020.3007928https://doaj.org/article/f53d8e74494d480fa64cbf9572ce15032020-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9136648/https://doaj.org/toc/2169-3536Medical imaging techniques play a critical role in diagnosing diseases and patient healthcare. They help in treatment, diagnosis, and early detection. Image segmentation is one of the most important steps in processing medical images, and it has been widely used in many applications. Multi-level thresholding (MLT) is considered as one of the simplest and most effective image segmentation techniques. Traditional approaches apply histogram methods; however, these methods face some challenges. In recent years, swarm intelligence methods have been leveraged in MLT, which is considered an NP-hard problem. One of the main drawbacks of the SI methods is when searching for optimum solutions, and some may get stuck in local optima. This because during the run of SI methods, they create random sequences among different operators. In this study, we propose a hybrid SI based approach that combines the features of two SI methods, marine predators algorithm (MPA) and moth-?ame optimization (MFO). The proposed approach is called MPAMFO, in which, the MFO is utilized as a local search method for MPA to avoid trapping at local optima. The MPAMFO is proposed as an MLT approach for image segmentation, which showed excellent performance in all experiments. To test the performance of MPAMFO, two experiments were carried out. The first one is to segment ten natural gray-scale images. The second experiment tested the MPAMFO for a real-world application, such as CT images of COVID-19. Therefore, thirteen CT images were used to test the performance of MPAMFO. Furthermore, extensive comparisons with several SI methods have been implemented to examine the quality and the performance of the MPAMFO. Overall experimental results confirm that the MPAMFO is an efficient MLT approach that approved its superiority over other existing methods.Mohamed Abd ElazizAhmed A. EweesDalia YousriHusein S. Naji AlwerfaliQamar A. AwadSongfeng LuMohammed A. A. Al-QanessIEEEarticleImage segmentationmulti-level thresholdingmoth-?ame optimization (MFO)marine predators algorithm (MPA)COVID-19swarm intelligenceElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 8, Pp 125306-125330 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Image segmentation multi-level thresholding moth-?ame optimization (MFO) marine predators algorithm (MPA) COVID-19 swarm intelligence Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Image segmentation multi-level thresholding moth-?ame optimization (MFO) marine predators algorithm (MPA) COVID-19 swarm intelligence Electrical engineering. Electronics. Nuclear engineering TK1-9971 Mohamed Abd Elaziz Ahmed A. Ewees Dalia Yousri Husein S. Naji Alwerfali Qamar A. Awad Songfeng Lu Mohammed A. A. Al-Qaness An Improved Marine Predators Algorithm With Fuzzy Entropy for Multi-Level Thresholding: Real World Example of COVID-19 CT Image Segmentation
description	Medical imaging techniques play a critical role in diagnosing diseases and patient healthcare. They help in treatment, diagnosis, and early detection. Image segmentation is one of the most important steps in processing medical images, and it has been widely used in many applications. Multi-level thresholding (MLT) is considered as one of the simplest and most effective image segmentation techniques. Traditional approaches apply histogram methods; however, these methods face some challenges. In recent years, swarm intelligence methods have been leveraged in MLT, which is considered an NP-hard problem. One of the main drawbacks of the SI methods is when searching for optimum solutions, and some may get stuck in local optima. This because during the run of SI methods, they create random sequences among different operators. In this study, we propose a hybrid SI based approach that combines the features of two SI methods, marine predators algorithm (MPA) and moth-?ame optimization (MFO). The proposed approach is called MPAMFO, in which, the MFO is utilized as a local search method for MPA to avoid trapping at local optima. The MPAMFO is proposed as an MLT approach for image segmentation, which showed excellent performance in all experiments. To test the performance of MPAMFO, two experiments were carried out. The first one is to segment ten natural gray-scale images. The second experiment tested the MPAMFO for a real-world application, such as CT images of COVID-19. Therefore, thirteen CT images were used to test the performance of MPAMFO. Furthermore, extensive comparisons with several SI methods have been implemented to examine the quality and the performance of the MPAMFO. Overall experimental results confirm that the MPAMFO is an efficient MLT approach that approved its superiority over other existing methods.
format	article
author	Mohamed Abd Elaziz Ahmed A. Ewees Dalia Yousri Husein S. Naji Alwerfali Qamar A. Awad Songfeng Lu Mohammed A. A. Al-Qaness
author_facet	Mohamed Abd Elaziz Ahmed A. Ewees Dalia Yousri Husein S. Naji Alwerfali Qamar A. Awad Songfeng Lu Mohammed A. A. Al-Qaness
author_sort	Mohamed Abd Elaziz
title	An Improved Marine Predators Algorithm With Fuzzy Entropy for Multi-Level Thresholding: Real World Example of COVID-19 CT Image Segmentation
title_short	An Improved Marine Predators Algorithm With Fuzzy Entropy for Multi-Level Thresholding: Real World Example of COVID-19 CT Image Segmentation
title_full	An Improved Marine Predators Algorithm With Fuzzy Entropy for Multi-Level Thresholding: Real World Example of COVID-19 CT Image Segmentation
title_fullStr	An Improved Marine Predators Algorithm With Fuzzy Entropy for Multi-Level Thresholding: Real World Example of COVID-19 CT Image Segmentation
title_full_unstemmed	An Improved Marine Predators Algorithm With Fuzzy Entropy for Multi-Level Thresholding: Real World Example of COVID-19 CT Image Segmentation
title_sort	improved marine predators algorithm with fuzzy entropy for multi-level thresholding: real world example of covid-19 ct image segmentation
publisher	IEEE
publishDate	2020
url	https://doaj.org/article/f53d8e74494d480fa64cbf9572ce1503
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An Improved Marine Predators Algorithm With Fuzzy Entropy for Multi-Level Thresholding: Real World Example of COVID-19 CT Image Segmentation

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