Renyi’s entropy based multilevel thresholding using a novel meta-heuristics algorithm

Liu, Wei, Huang, Yongkun, Ye, Zhiwei, Cai, Wencheng, Yang, Shuai, Cheng, Xiaochun ORCID: https://orcid.org/0000-0003-0371-9646 and Frank, Ibrahim (2020) Renyi’s entropy based multilevel thresholding using a novel meta-heuristics algorithm. Applied Sciences, 10 (9) , 3225. ISSN 2076-3417 (doi:10.3390/app10093225)

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Abstract

Multi-level image thresholding is the most direct and effective method for image segmentation, which is a key step for image analysis and computer vision, however, as the number of threshold values increases, exhaustive search does not work efficiently and effectively and evolutionary algorithms often fall into a local optimal solution. In the paper, a meta-heuristics algorithm based on the breeding mechanism of Chinese hybrid rice is proposed to seek the optimal multi-level thresholds for image segmentation and Renyi’s entropy is utilized as the fitness function. Experiments have been run on four scanning electron microscope images of cement and four standard images, moreover, it is compared with other six classical and novel evolutionary algorithms: genetic algorithm, particle swarm optimization algorithm, differential evolution algorithm, ant lion optimization algorithm, whale optimization algorithm, and salp swarm algorithm. Meanwhile, some indicators, including the average fitness values, standard deviation, peak signal to noise ratio, and structural similarity index are used as evaluation criteria in the experiments. The experimental results show that the proposed method prevails over the other algorithms involved in the paper on most indicators and it can segment cement scanning electron microscope image effectively.

Item Type: Article
Additional Information: This article belongs to the Special Issue Intelligent Processing on Image and Optical Information, Volume Ⅱ
Keywords (uncontrolled): image segmentation, multi-level thresholding, Renyi’s entropy, meta-heuristics algorithm
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 29857
Notes on copyright: © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
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Depositing User: Jisc Publications Router
Date Deposited: 11 May 2020 14:18
Last Modified: 12 May 2020 21:00
URI: https://eprints.mdx.ac.uk/id/eprint/29857

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