Recent nature-inspired algorithms for medical image segmentation based on Tsallis statistics

DOI10.1016/J.CNSNS.2020.105256zbMath1478.92110OpenAlexW3012596442MaRDI QIDQ2208087

N. T. Saito, P. S. Rodrigues, R. M. Santos, G. A. Wachs-Lopes

Publication date: 23 October 2020

Published in: Communications in Nonlinear Science and Numerical Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cnsns.2020.105256

zbMATH Keywords

optimizationTsallis entropybio-inspired algorithmsmedical image segmentationnon-extensive theory

Mathematics Subject Classification ID

Approximation methods and heuristics in mathematical programming (90C59) Biomedical imaging and signal processing (92C55)

Uses Software

• Krill herd
• WOA
• GWO
• GOA

Cites Work

• Optimization by Simulated Annealing
• A new entropy based on a group-theoretical structure
• Krill herd: A new bio-inspired optimization algorithm
• Possible generalization of Boltzmann-Gibbs statistics.
• Distributions and channel capacities in generalized statistical mechanics
• Improving a firefly meta-heuristic for multilevel image segmentation using Tsallis entropy
• Metaheuristic Optimization: Nature-Inspired Algorithms and Applications
• Tabu Search—Part I
• Nonextensive entropy measure of EEG following brain injury from cardiac arrest

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