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Comparison of diversity indices applied to macrophyte incidence-based data BABT
Evangelista,Heloísa Beatriz Antoniazi; Thomaz,Sidinei Magela; Evangelista,Luiz Roberto.
In this work, a recently proposed diversity index based on Patil and Taillie parametric diversity measure (or Tsallis entropy), Sq*, was applied to samples (presence-absence data) of macrophytes from the Itaipu Reservoir, Brazil. This new index was the value of the family of indices Sq for a specific evenness of a sample. Results demonstrated that the Shannon index and species richness showed expressively high correlation with the Sq*; however, the evenness had low correlation coefficients with the index Sq*, indicating that Sq* was particularly sensitive to rarity and species richness. On the other hand, the weak correlations of this index with evenness demonstrated that it was less sensitive to species relative abundances.
Tipo: Info:eu-repo/semantics/article Palavras-chave: Tsallis entropy; Macrophytes; Reservoir; Shannon; Simpson.
Ano: 2012 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132012000200014
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Brain tissue segmentation using q-entropy in multiple sclerosis magnetic resonance images BJMBR
Diniz,P.R.B.; Murta-Junior,L.O.; Brum,D.G.; de Araújo,D.B.; Santos,A.C..
The loss of brain volume has been used as a marker of tissue destruction and can be used as an index of the progression of neurodegenerative diseases, such as multiple sclerosis. In the present study, we tested a new method for tissue segmentation based on pixel intensity threshold using generalized Tsallis entropy to determine a statistical segmentation parameter for each single class of brain tissue. We compared the performance of this method using a range of different q parameters and found a different optimal q parameter for white matter, gray matter, and cerebrospinal fluid. Our results support the conclusion that the differences in structural correlations and scale invariant similarities present in each tissue class can be accessed by generalized...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Multiple sclerosis; Automatic segmentation; Magnetic resonance image; Volumetry; Tsallis entropy.
Ano: 2010 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2010000100011
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