Jump to main content
US EPA
United States Environmental Protection Agency
Search
Search
Main menu
Environmental Topics
Laws & Regulations
About EPA
Health & Environmental Research Online (HERO)
Contact Us
Print
Feedback
Export to File
Search:
This record has one attached file:
Add More Files
Attach File(s):
Display Name for File*:
Save
Citation
Tags
HERO ID
8607298
Reference Type
Journal Article
Title
Improved manta ray foraging optimization for multi-level thresholding using COVID-19 CT images
Author(s)
Houssein, EH; Emam, MM; Ali, AA
Year
2021
Is Peer Reviewed?
1
Journal
Neural Computing and Applications
ISSN:
0941-0643
Page Numbers
1-21
Language
English
PMID
34248291
DOI
10.1007/s00521-021-06273-3
Web of Science Id
WOS:000670722000001
Abstract
Coronavirus disease 2019 (COVID-19) is pervasive worldwide, posing a high risk to people's safety and health. Many algorithms were developed to identify COVID-19. One way of identifying COVID-19 is by computed tomography (CT) images. Some segmentation methods are proposed to extract regions of interest from COVID-19 CT images to improve the classification. In this paper, an efficient version of the recent manta ray foraging optimization (MRFO) algorithm is proposed based on the oppositionbased learning called the MRFO-OBL algorithm. The original MRFO algorithm can stagnate in local optima and requires further exploration with adequate exploitation. Thus, to improve the population variety in the search space, we applied Opposition-based learning (OBL) in the MRFO's initialization step. MRFO-OBL algorithm can solve the image segmentation problem using multilevel thresholding. The proposed MRFO-OBL is evaluated using Otsu's method over the COVID-19 CT images and compared with six meta-heuristic algorithms: sine-cosine algorithm, moth flame optimization, equilibrium optimization, whale optimization algorithm, slap swarm algorithm, and original MRFO algorithm. MRFO-OBL obtained useful and accurate results in quality, consistency, and evaluation matrices, such as peak signal-to-noise ratio and structural similarity index. Eventually, MRFO-OBL obtained more robustness for the segmentation than all other algorithms compared. The experimental results demonstrate that the proposed method outperforms the original MRFO and the other compared algorithms under Otsu's method for all the used metrics.
Keywords
COVID-19 CT images; Otsu's method; Multilevel thresholding image segmentation; Manta ray foraging optimization; Meta-heuristics algorithms
Home
Learn about HERO
Using HERO
Search HERO
Projects in HERO
Risk Assessment
Transparency & Integrity