A novel equilibrium optimization algorithm for multi-thresholding image segmentation problems

Abstract

Image segmentation is considered a crucial step required for image analysis and research. Many techniques have been proposed to resolve the existing problems and improve the quality of research, such as region-based, threshold-based, edge-based, and feature-based clustering in the literature. The researchers have moved toward using the threshold technique due to the ease of use for image segmentation. To find the optimal threshold value for a grayscale image, we improved and used a novel meta-heuristic equilibrium algorithm to resolve this scientific problem. Additionally, our improved algorithm has the ability to enhance the accuracy of the segmented image for research analysis with a significant threshold level. The performance of our algorithm is compared with seven other algorithms like whale optimization algorithm, bat algorithm, sine–cosine algorithm, salp swarm algorithm, Harris hawks algorithm, crow search algorithm, and particle swarm optimization. Based on a set of well-known test images taken from Berkeley Segmentation Dataset, the performance evaluation of our algorithm and well-known algorithms described above has been conducted and compared. According to the independent results and analysis of each algorithm, our algorithm can outperform all other algorithms in fitness values, peak signal-to-noise ratio metric, structured similarity index metric, maximum absolute error, and signal-to-noise ratio. However, our algorithm cannot outperform some algorithms in standard deviation values and central processing unit time with the large threshold levels observed.

Publication DOI: https://doi.org/10.1007/s00521-020-04820-y
Divisions: College of Business and Social Sciences > Aston Business School
College of Business and Social Sciences > Aston Business School > Operations & Information Management
Funding Information: This funding was supported by VC Research, VCR 0000016.
Additional Information: © Springer Nature B.V. 2020. The final publication is available at Springer via http://dx.doi.org/10.1007/s00521-020-04820-y Funding Information: This funding was supported by VC Research, VCR 0000016.
Uncontrolled Keywords: Equilibrium optimization algorithm (EOA),Image segmentation problem,Kapur’s entropy,Software,Artificial Intelligence
Publication ISSN: 1433-3058
Last Modified: 22 Apr 2024 07:30
Date Deposited: 09 Jun 2022 10:37
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://link.sp ... 521-020-04820-y (Publisher URL)
PURE Output Type: Article
Published Date: 2021-09-01
Published Online Date: 2020-03-16
Accepted Date: 2020-02-24
Authors: Abdel-Basset, Mohamed
Chang, Victor (ORCID Profile 0000-0002-8012-5852)
Mohamed, Reda

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