Cuckoo search with differential evolution mutation and Masi entropy for multi-level image segmentation

2021 ◽  
Author(s):  
Swarnajit Ray ◽  
Santanu Parai ◽  
Arunita Das ◽  
Krishna Gopal Dhal ◽  
Prabir Kumar Naskar
Keyword(s):  
Image Segmentation ◽  
Differential Evolution ◽  
Cuckoo Search ◽  
Multi Level ◽  
Masi Entropy

SOM-Like Neural Network and Differential Evolution for Multi-level Image Segmentation and Classification in Slit-Lamp Images

2018 ◽  
pp. 26-37 ◽  
Author(s):  
Hans Israel Morales-Lopez ◽  
Israel Cruz-Vega ◽  
Juan Manuel Ramirez-Cortes ◽  
Hayde Peregrina-Barreto ◽  
Jose Rangel-Magdaleno
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Differential Evolution ◽  
Slit Lamp ◽  
Multi Level

scholarly journals A Grouping Differential Evolution Algorithm Boosted by Attraction and Repulsion Strategies for Masi Entropy-Based Multi-Level Image Segmentation

Entropy ◽  
2021 ◽  
Vol 24 (1) ◽  
pp. 8
Author(s):  
Seyed Jalaleddin Mousavirad ◽  
Davood Zabihzadeh ◽  
Diego Oliva ◽  
Marco Perez-Cisneros ◽  
Gerald Schaefer
Keyword(s):  
Differential Evolution ◽  
Clustering Algorithm ◽  
Differential Evolution Algorithm ◽  
Similarity Index ◽  
Population Based ◽  
Function Evaluation ◽  
Image Thresholding ◽  
Current Population ◽  
Multi Level ◽  
Masi Entropy

Masi entropy is a popular criterion employed for identifying appropriate threshold values in image thresholding. However, with an increasing number of thresholds, the efficiency of Masi entropy-based multi-level thresholding algorithms becomes problematic. To overcome this, we propose a novel differential evolution (DE) algorithm as an effective population-based metaheuristic for Masi entropy-based multi-level image thresholding. Our ME-GDEAR algorithm benefits from a grouping strategy to enhance the efficacy of the algorithm for which a clustering algorithm is used to partition the current population. Then, an updating strategy is introduced to include the obtained clusters in the current population. We further improve the algorithm using attraction (towards the best individual) and repulsion (from random individuals) strategies. Extensive experiments on a set of benchmark images convincingly show ME-GDEAR to give excellent image thresholding performance, outperforming other metaheuristics in 37 out of 48 cases based on cost function evaluation, 26 of 48 cases based on feature similarity index, and 20 of 32 cases based on Dice similarity. The obtained results demonstrate that population-based metaheuristics can be successfully applied to entropy-based image thresholding and that strengthening both exploitation and exploration strategies, as performed in ME-GDEAR, is crucial for designing such an algorithm.

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