Brain magnetic resonance imaging segmentation is accomplished in this work by applying nonparametric density estimation, using the mean shift algorithm in the joint spatial-range domain. The quality of the class boundaries is improved by including an edge confidence map, that represents the confidence of truly being in the presence of a border between adjacent regions; an adjacency graph is then constructed with the labeled regions, and analyzed and pruned to merge adjacent regions. In order to assign image regions to a cerebral tissue type, a spatial normalization between image data and standard probability maps is carried out, so that for each structure a maximum a posteriori probability criterion is applied. The method was applied to synthetic and real images, keeping all parameters constant throughout the process for each type of data. The combination of region segmentation and edge detection proved to be a robust technique, as adequate clusters were automatically identified, regardless of the noise level and bias. In a comparison with reference segmentations, average Tanimoto indexes of 0.90-0.99 were obtained for synthetic data and of 0.59-0.99 for real data, considering gray matter, white matter, and background.
In the present study, we examined two baroreflex sensitivity (BRS) issues that remain uncertain: the differences among diverse BRS assessment techniques and the association between BRS and vagal outflow. Accordingly, the electrocardiogram and non-invasive arterial pressure were recorded in 27 healthy subjects, during supine with and without controlled breathing, standing, exercise, and recovery conditions. Vagal outflow was estimated by heart rate variability indexes, whereas BRS was computed by alpha-coefficient, transfer function, complex demodulation in low- and high-frequency bands, and by sequence technique. Our results indicated that only supine maneuvers showed significantly greater BRS values over the high frequency than in the low-frequency band. For maneuvers at the same frequency region, supine conditions presented a larger number of significant differences among techniques. The plots between BRS and vagal measures depicted a funnel-shaped relationship with significant log-log correlations (r=0.880-0.958). Very short latencies between systolic pressure and RR interval series in high-frequency band and strong log-log correlations between frequency bands were found. Higher variability among different baroreflex measurements was associated with higher level of vagal outflow. Methodological assumptions for each technique seem affected by non-baroreflex variation sources, and a modified responsiveness of vagal motoneurons due to distinct stimulation levels for each maneuver was suggested. Thus, highest vagal outflows corresponded to greatest BRS values, with maximum respiratory effect for the high-frequency band values. In conclusion, BRS values and differences across the tested techniques were strongly related to the vagal outflow induced by the maneuvers.
We present a discrete compactness (DC) index, together with a classification scheme, based both on the size and shape features extracted from brain volumes, to determine different aging stages: healthy controls (HC), mild cognitive impairment (MCI), and Alzheimer's disease (AD). A set of 30 brain magnetic resonance imaging (MRI) volumes for each group was segmented and two indices were measured for several structures: three-dimensional DC and normalized volumes (NVs). The discrimination power of these indices was determined by means of the area under the curve (AUC) of the receiver operating characteristic, where the proposed compactness index showed an average AUC of 0.7 for HC versus MCI comparison, 0.9 for HC versus AD separation, and 0.75 for MCI versus AD groups. In all cases, this index outperformed the discrimination capability of the NV. Using selected features from the set of DC and NV measures, three support vector machines were optimized and validated for the pairwise separation of the three classes. Our analysis shows classification rates of up to 98.3% between HC and AD, 85% between HC and MCI, and 93.3% for MCI and AD separation. These results outperform those reported in the literature and demonstrate the viability of the proposed morphological indices to classify different aging stages.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.