Volume 18, Issue 2 (June 2022)                   IJEEE 2022, 18(2): 55-65 | Back to browse issues page

XML Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Das G, Panda R, Samantaray L, Agrawal S. A Novel Non-Entropic Objective Function for Multilevel Optimal Threshold Selection Using Adaptive Equilibrium Optimizer. IJEEE 2022; 18 (2) :55-65
URL: http://ijeee.iust.ac.ir/article-1-2230-en.html
A Novel Non-Entropic Objective Function for Multilevel Optimal Threshold Selection Using Adaptive Equilibrium Optimizer
G. Das , R. Panda , L. Samantaray , S. Agrawal
Abstract:   (2212 Views)
Multilevel optimal threshold selection is important and comprehensively used in the area of image processing. Mostly, entropic information-based threshold selection techniques are used. These methods make use of the entropy of the distribution of the grey levels of an image. However, entropy functions largely depend on spatial distribution of the image. This makes the methods inefficient when the distribution of the grey information of an image is not uniform. To solve this problem, a novel non-entropic method for multilevel optimal threshold selection is proposed. In this contribution, simple numbers (pixel counts), explicitly free from the spatial distribution, are used. A novel non-entropic objective function is proposed. It is used for multilevel threshold selection by maximizing the partition score using the adaptive equilibrium method. A new theoretical derivation for the fitness function is highlighted. The key to the achievement is the exploitation of the score among classes, reinforcing an improvised threshold selection process. Standard test images are considered for the experiment. The performances are compared with state-of-the-art entropic value-based methods used for multilevel threshold assortment and are found better. It is revealed that the results obtained using the suggested technique are encouraging both qualitatively and quantitatively. The newly proposed method would be very useful for solving different real-world engineering optimization problems.
Keywords: Artificial Intelligence , Entropic Methods , Equilibrium Optimizer , Multilevel Threshold Selection
Full-Text [PDF 1280 kb]   (927 Downloads)    
  • A novel non-entropic objective function is proposed.
  • A novel non-entropic method for multilevel optimal threshold selection is proposed.
  • The performances are compared with the state-of-the-art entropic value-based method used for multilevel threshold assortment and are found better.


Type of Study: Research Paper | Subject: Biomedical Signal Processing
Received: 2021/07/04 | Revised: 2024/05/13 | Accepted: 2022/01/15
Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Creative Commons License
© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.