Shannon and Fuzzy entropy based evolutionary image thresholding for image segmentation
Image segmentation is a very important and pre-processing step in image analysis. The conventional multilevel thresholding methods are efficient for bi-level thresholding because of its simplicity, robustness, less convergence time and accuracy. However, a mass of computational cost is needed and ef...
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doaj-a320f227884d4a688735463af0c84e142021-06-02T10:16:04ZengElsevierAlexandria Engineering Journal1110-01682018-09-0157316431655Shannon and Fuzzy entropy based evolutionary image thresholding for image segmentationM.S.R. Naidu0P. Rajesh Kumar1K. Chiranjeevi2Department of Electronics and Communication Engineering, Aditya Institute of Technology and Management (AITAM), Tekkali, Andhra Pradesh, India; Corresponding author.Department of Electronics and Communication Engineering, A.U. College of Engineering (A), Andhra University, Visakhapatnam, Andhra Pradesh, IndiaDepartment of Electronics and Communication Engineering, GMR Institute of Technology, Rajam, Srikakulam, IndiaImage segmentation is a very important and pre-processing step in image analysis. The conventional multilevel thresholding methods are efficient for bi-level thresholding because of its simplicity, robustness, less convergence time and accuracy. However, a mass of computational cost is needed and efficiency is broken down as an exhaustive search is utilized for finding the optimal thresholds, which results in application of evolutionary algorithm and swarm intelligence to obtain the optimal thresholds. The main aim of image segmentation was to segregate the foreground from background. For the first time this paper established a naturally inspired firefly algorithm based multilevel image thresholding for image segmentation by maximizing Shannon entropy or Fuzzy entropy. The proposed algorithm is tested on standard set of images and results are compared with the Shannon entropy or Fuzzy entropy based methods that are optimized by Differential Evolution (DE), Particle Swarm Optimization (PSO) and bat algorithm (BA). It is demonstrated that the proposed method shows better performance in objective function, structural similarity index, peak signal to noise ratio, misclassification error and CPU time than state of art methods. Keywords: Image segmentation, Image thresholding, Fuzzy entropy, Shannon entropy, Particle Swarm Optimization, Firefly algorithmhttp://www.sciencedirect.com/science/article/pii/S1110016817301886 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
M.S.R. Naidu P. Rajesh Kumar K. Chiranjeevi |
spellingShingle |
M.S.R. Naidu P. Rajesh Kumar K. Chiranjeevi Shannon and Fuzzy entropy based evolutionary image thresholding for image segmentation Alexandria Engineering Journal |
author_facet |
M.S.R. Naidu P. Rajesh Kumar K. Chiranjeevi |
author_sort |
M.S.R. Naidu |
title |
Shannon and Fuzzy entropy based evolutionary image thresholding for image segmentation |
title_short |
Shannon and Fuzzy entropy based evolutionary image thresholding for image segmentation |
title_full |
Shannon and Fuzzy entropy based evolutionary image thresholding for image segmentation |
title_fullStr |
Shannon and Fuzzy entropy based evolutionary image thresholding for image segmentation |
title_full_unstemmed |
Shannon and Fuzzy entropy based evolutionary image thresholding for image segmentation |
title_sort |
shannon and fuzzy entropy based evolutionary image thresholding for image segmentation |
publisher |
Elsevier |
series |
Alexandria Engineering Journal |
issn |
1110-0168 |
publishDate |
2018-09-01 |
description |
Image segmentation is a very important and pre-processing step in image analysis. The conventional multilevel thresholding methods are efficient for bi-level thresholding because of its simplicity, robustness, less convergence time and accuracy. However, a mass of computational cost is needed and efficiency is broken down as an exhaustive search is utilized for finding the optimal thresholds, which results in application of evolutionary algorithm and swarm intelligence to obtain the optimal thresholds. The main aim of image segmentation was to segregate the foreground from background. For the first time this paper established a naturally inspired firefly algorithm based multilevel image thresholding for image segmentation by maximizing Shannon entropy or Fuzzy entropy. The proposed algorithm is tested on standard set of images and results are compared with the Shannon entropy or Fuzzy entropy based methods that are optimized by Differential Evolution (DE), Particle Swarm Optimization (PSO) and bat algorithm (BA). It is demonstrated that the proposed method shows better performance in objective function, structural similarity index, peak signal to noise ratio, misclassification error and CPU time than state of art methods. Keywords: Image segmentation, Image thresholding, Fuzzy entropy, Shannon entropy, Particle Swarm Optimization, Firefly algorithm |
url |
http://www.sciencedirect.com/science/article/pii/S1110016817301886 |
work_keys_str_mv |
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