Comparison of diversity indices applied to macrophyte incidence-based data

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.

Brain tissue segmentation using q-entropy in multiple sclerosis magnetic resonance images

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 Tsallis entropy, obtaining the intensity limits for these tissue class separations. In order to test this method, we used it for analysis of brain magnetic resonance images of 43 patients and 10 healthy controls matched for gender and age. The values found for the entropic q index were 0.2 for cerebrospinal fluid, 0.1 for white matter and 1.5 for gray matter. With this algorithm, we could detect an annual loss of 0.98 percent for the patients, in agreement with literature data. Thus, we can conclude that the entropy of Tsallis adds advantages to the process of automatic target segmentation of tissue classes, which had not been demonstrated previously.