3 resultados para Minimum distance
em Biblioteca Digital da Produção Intelectual da Universidade de São Paulo
Resumo:
PURPOSE. We previously demonstrated that most eyes have regionally variable extensions of Bruch's membrane (BM) inside the clinically identified disc margin (DM) that are clinically and photographically invisible. We studied the impact of these findings on DM- and BM opening (BMO)-derived neuroretinal rim parameters. METHODS. Disc stereo-photography and spectral domain optical coherence tomography (SD-OCT, 24 radial B-scans centered on the optic nerve head) were performed on 30 glaucoma patients and 10 age-matched controls. Photographs were colocalized to SD-OCT data such that the DM and BMO could be visualized in each B-scan. Three parameters were computed: (1) DM-horizontal rim width (HRW), the distance between the DM and internal limiting membrane (ILM) along the DM reference plane; (2) BMO-HRW, the distance between BMO and ILM along the BMO reference plane; and (3) BMO-minimum rim width (MRW), the minimum distance between BMO and ILM. Rank-order correlations of sectors ranked by rim width and spatial concordance measured as angular distances between equivalently ranked sectors were derived. RESULTS. The average DM position was external to BMO in all quadrants, except inferotemporally. There were significant sectoral differences among all three rim parameters. DM- HRW and BMO-HRW sector ranks were better correlated (median rho = 0.84) than DM- HRW and BMO-MRW (median rho = 0.55), or BMO-HRW and BMO-MRW (median rho = 0.60) ranks. Sectors with the narrowest BMO-MRW were infrequently the same as those with the narrowest DM-HRW or BMO-HRW. CONCLUSIONS. BMO-MRW quantifies the neuroretinal rim from a true anatomical outer border and accounts for its variable trajectory at the point of measurement. (Invest Ophthalmol Vis Sci. 2012;53:1852-1860) DOI:10.1167/iovs.11-9309
Resumo:
The aim of this study was to determine whether image artifacts caused by orthodontic metal accessories interfere with the accuracy of 3D CBCT model superimposition. A human dry skull was subjected three times to a CBCT scan: at first without orthodontic brackets (T1), then with stainless steel brackets bonded without (T2) and with orthodontic arch wires (T3) inserted into the brackets' slots. The registration of image surfaces and the superimposition of 3D models were performed. Within-subject surface distances between T1-T2, T1-T3 and T2-T3 were computed and calculated for comparison among the three data sets. The minimum and maximum Hausdorff Distance units (HDu) computed between the corresponding data points of the T1 and T2 CBCT 3D surface images were 0.000000 and 0.049280 HDu, respectively, and the mean distance was 0.002497 HDu. The minimum and maximum Hausdorff Distances between T1 and T3 were 0.000000 and 0.047440 HDu, respectively, with a mean distance of 0.002585 HDu. In the comparison between T2 and T3, the minimum, maximum and mean Hausdorff Distances were 0.000000, 0.025616 and 0.000347 HDu, respectively. In the current study, the image artifacts caused by metal orthodontic accessories did not compromise the accuracy of the 3D model superimposition. Color-coded maps of overlaid structures complemented the computed Hausdorff Distances and demonstrated a precise fusion between the data sets.
Resumo:
Estimators of home-range size require a large number of observations for estimation and sparse data typical of tropical studies often prohibit the use of such estimators. An alternative may be use of distance metrics as indexes of home range. However, tests of correlation between distance metrics and home-range estimators only exist for North American rodents. We evaluated the suitability of 3 distance metrics (mean distance between successive captures [SD], observed range length [ORL], and mean distance between all capture points [AD]) as indexes for home range for 2 Brazilian Atlantic forest rodents, Akodon montensis (montane grass mouse) and Delomys sublineatus (pallid Atlantic forest rat). Further, we investigated the robustness of distance metrics to low numbers of individuals and captures per individual. We observed a strong correlation between distance metrics and the home-range estimator. None of the metrics was influenced by the number of individuals. ORL presented a strong dependence on the number of captures per individual. Accuracy of SD and AD was not dependent on number of captures per individual, but precision of both metrics was low with numbers of captures below 10. We recommend the use of SD and AD instead of ORL and use of caution in interpretation of results based on trapping data with low captures per individual.