Recent Nature-Inspired Algorithms for Medical Image Segmentation Based on Tsallis Statistics
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Tipo de produção
Artigo
Data
2020-03-09
Autores
LOPES, GUILHERME ALBERTO WACHS
SANTOS, R. M.
SAITO, N.T.
RODRIGUES, P. S.
SANTOS, R. M.
SAITO, N.T.
RODRIGUES, P. S.
Orientador
https://www.sciencedirect.com/science/article/pii/S1007570420300897
Medical image segmentation
Medical image segmentation
Periódico
Communications in Nonlinear Science and Numerical Simulation
Título da Revista
ISSN da Revista
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Citação
LOPES, G. A. W., G.A.; SANTOS, R M.; SAITO, N. T.; RODRIGUES, P. S. Recent nature-inspired algorithms for medical image segmentation based on Tsallis statistics. Communications in Nonlinear Science and Numerical Simulation, v. 1, p. 105256, sept. 2020.
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Palavras-chave
Tsallis entropy,Non-Extensive theory,Bio-Inspired algorithms,Optimization
Resumo
Recently, many algorithms have emerged inspired by the biological behavior of animal life to deal with complicated applications such as combinatorial optimization. One of the most critical discussions involving these algorithms is concerning their objective functions. Also, recently, many works have demonstrated the efficiency of Tsallis non-extensive statistics in several applications. However, this formalism has not yet been tested in most recent bio-inspired algorithms as an evaluation function. Thus, this paper presents a study of seven of the most promising bio-inspired algorithms recently proposed (a maximum one decade), from this entropy applied to the multi-thresholding segmentation of medical im- ages. The results show the range of values of q , the so-called non-extensivity parameter of the Tsallis entropy, for which the algorithms tested here have their best performance. It is also demonstrated that the Firefly algorithm (FFA) is the one that obtained the best per- formance in terms of segmentation, and Grey Wolf Optimizer (GWO) presents the fastest convergence.