BackgroundIndependence between observations is a standard prerequisite of traditional statistical tests of association. This condition is, however, violated when autocorrelation is present within the data. In the case of variables that are regularly sampled in space (i.e. lattice data or images), such as those provided by remote-sensing or geographical databases, this problem is particularly acute. Because analytic derivation of the null probability distribution of the test statistic (e.g. Pearson's r) is not always possible when autocorrelation is present, we propose instead the use of a Monte Carlo simulation with surrogate data.Methodology/Principal FindingsThe null hypothesis that two observed mapped variables are the result of independent pattern generating processes is tested here by generating sets of random image data while preserving the autocorrelation function of the original images. Surrogates are generated by matching the dual-tree complex wavelet spectra (and hence the autocorrelation functions) of white noise images with the spectra of the original images. The generated images can then be used to build the probability distribution function of any statistic of association under the null hypothesis. We demonstrate the validity of a statistical test of association based on these surrogates with both actual and synthetic data and compare it with a corrected parametric test and three existing methods that generate surrogates (randomization, random rotations and shifts, and iterative amplitude adjusted Fourier transform). Type I error control was excellent, even with strong and long-range autocorrelation, which is not the case for alternative methods.Conclusions/SignificanceThe wavelet-based surrogates are particularly appropriate in cases where autocorrelation appears at all scales or is direction-dependent (anisotropy). We explore the potential of the method for association tests involving a lattice of binary data and discuss its potential for validation of species distribution models. An implementation of the method in Java for the generation of wavelet-based surrogates is available online as supporting material.

Three-dimensional (3-D) large-scale imaging of microvascular networks is of interest in various areas of biology and medicine related to structural, functional, developmental, and pathological issues. Light-sheet fluorescence microscopy (LSFM) techniques are rapidly spreading and are now on the way to offer operational solutions for large-scale tissue imaging. This contribution describes how reliable vessel segmentation can be handled from LSFM data in very large tissue volumes using a suitable image analysis workflow. Since capillaries are tubular objects of a few microns scale radius, they represent challenging structures to reliably reconstruct without distortion and artifacts. We provide a systematic analysis of multiview deconvolution image processing workflow to control and evaluate the accuracy of the reconstructed vascular network using various low to high level, metrics. We show that even if low-level structural metrics are sensitive to isotropic imaging enhancement provided by a larger number of views, functional high-level metrics, including perfusion permeability, are less sensitive. Hence, combining deconvolution and registration onto a few number of views appears sufficient for a reliable quantitative 3-D vessel segmentation for their possible use for perfusion modeling.