MBIS: Multivariate Bayesian Image Segmentation Tool

We present MBIS (Multivariate Bayesian Image Segmentation tool), a clustering tool based on the mixture of multivariate normal distributions model. MBIS supports multi-channel bias field correction based on a B-spline model. A second methodological novelty is the inclusion of graph-cuts optimization for the stationary anisotropic hidden Markov random field model. Along with MBIS, we release an evaluation framework that contains three different experiments on multi-site data. We first validate the accuracy of segmentation and the estimated bias field for each channel. MBIS outperforms a widely used segmentation tool in a cross-comparison evaluation. The second experiment demonstrates the robustness of results on atlas-free segmentation of two image sets from scan-rescan protocols on 21 healthy subjects. Multivariate segmentation is more replicable than the monospectral counterpart on T1-weighted images. Finally, we provide a third experiment to illustrate how MBIS can be used in a large-scale study of tissue volume change with increasing age in 584 healthy subjects. This last result is meaningful as multivariate segmentation performs robustly without the need for prior knowledge.

Comparison of different pre-treatments to improve accuracy of total soluble solids.

The applicability of a portable NIR spectrometer for estimating the °Brix content of grapes by non-destructive measurement has been analysed in field. The NIR spectrometer AOTF-NIR Luminar 5030, from Brimrose, was used. The spectrometer worked with a spectral range from 1100 to 2300 nm. A total of 600 samples of Cabernet Sauvignon grapes, belonging to two vintages, were measured in a non-destructive way. The specific objective of this research is to analyse the influence of the statistical treatment of the spectra information in the development of °Brix estimation models. Different data pretreatments have been tested before applying multivariate analysis techniques to generate estimation models. The calibration using PLS regression applied to spectra data pretreated with the MSC method (multiplicative scatter correction) has been the procedure with better results. Considering the models developed with data corresponding to the first campaign, errors near to 1.35 °Brix for calibration (SEC = 1.36) and, about 1.50 °Brix for validation (SECV = 1.52) were obtained. The coefficients of determination were R2 = 0.78 for the calibration, and R2 = 0.77 for the validation. In addition, the great variability in the data of the °Brix content for the tested plots was analysed. The variation of °Brix on the plots was up to 4 °Brix, for all varieties. This deviation was always superior to the calculated errors in the generated models. Therefore, the generated models can be considered to be valid for its application in field. Models were validated with data corresponding to the second campaign. In this sense, the validation results were worse than those obtained in the first campaign. It is possible to conclude in the need to realize an adjustment of the spectrometer for each season, and to develop specific predictive models for every vineyard.