Recent Nature-Inspired Algorithms for Medical Image Segmentation Based on Tsallis Statistics

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dc.contributor.advisorhttps://www.sciencedirect.com/science/article/pii/S1007570420300897
dc.contributor.advisorMedical image segmentation
dc.contributor.authorLOPES, GUILHERME ALBERTO WACHS
dc.contributor.authorSANTOS, R. M.
dc.contributor.authorSAITO, N.T.
dc.contributor.authorRODRIGUES, P. S.
dc.contributor.authorOrcidhttps://orcid.org/0000-0003-0873-3236
dc.contributor.authorOrcidhttps://orcid.org/0000-0003-3258-0794
dc.date.accessioned2021-10-19T18:26:48Z
dc.date.available2021-10-19T18:26:48Z
dc.date.issued2020-03-09
dc.description.abstractRecently, 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.
dc.description.firstpage105256
dc.description.volume1
dc.identifier.citationLOPES, 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.
dc.identifier.doi10.1016/j.cnsns.2020.105256
dc.identifier.issn1007-5704
dc.identifier.urihttps://repositorio.fei.edu.br/handle/FEI/3402
dc.relation.ispartofCommunications in Nonlinear Science and Numerical Simulation
dc.rightsAcesso Restrito
dc.subjectTsallis entropy
dc.subjectNon-Extensive theory
dc.subjectBio-Inspired algorithms
dc.subjectOptimization
dc.titleRecent Nature-Inspired Algorithms for Medical Image Segmentation Based on Tsallis Statisticspt_BR
dc.typeArtigopt_BR
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