Testing Pairwise Association between Spatially Autocorrelated Variables: A New Approach Using Surrogate Lattice Data

International audienceBackground: Independence 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 Findings: The 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/Significance: The 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

Automatic identification of cell files in light microscopic images of conifer wood

International audienceIn this paper, we present an automatic method to recognize cell files in light microscopic images of conifer wood. This original method is decomposed into three steps: the segmentation step which extracts some anatomical structures in the image, the classification step which identifies in these structures the interesting cells, and the cell files recognition step. Some preliminary results obtained on several species of conifers are presented and analyzed

Toward quantitative three-dimensional microvascular networks segmentation with multiview light-sheet fluorescence microscopy

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

From whole-organ imaging to in-silico blood flow modeling: a new multi-scale network analysis for revisiting tissue functional anatomy

We present a multi-disciplinary image-based blood flow perfusion modeling of a whole organ vascular network for analyzing both its structural and functional properties. We show how the use of Light-Sheet Fluorescence Microscopy (LSFM) permits whole-organ micro- vascular imaging, analysis and modelling. By using adapted image post-treatment workflow, we could segment, vectorize and reconstruct the entire micro-vascular network composed of 1.7 million vessels, from the tissue-scale, inside a * 25 × 5 × 1 = 125mm 3 volume of the mouse fat pad, hundreds of times larger than previous studies, down to the cellular scale at micron resolution, with the entire blood perfusion modeled. Adapted network analysis revealed the structural and functional organization of meso-scale tissue as strongly connected communities of vessels. These communities share a distinct heterogeneous core region and a more homogeneous peripheral region, consistently with known biological functions of fat tissue. Graph clustering analysis also revealed two distinct robust meso-scale typical sizes (from 10 to several hundred times the cellular size), revealing, for the first time, strongly connected functional vascular communities. These community networks support heterogeneous micro-environments. This work provides the proof of concept that in-silico all-tissue perfusion modeling can reveal new structural and functional exchanges between micro-regions in tissues, found from community clusters in the vascular graph

Textures characterization based on complex wavelet transform for image segmentation : applications on remote sensing images and forest ecology

L'analyse des images numériques, bien que largement étudiée, reste encore aujourd'hui un réel défi. Avec pour objectifs la description pertinente et la reconnaissance sémantique du contenu de celles-ci, de nombreuses applications requièrent une attention particulière quant à cette analyse. Pour répondre à ces besoins, l'analyse du contenu des images est réalisée de façon automatique grâce à des méthodes informatiques se rapprochant par exemple des mathématiques, des statistiques, de la physique. Une façon pertinente et reconnue de représenter les objets observés dans les images réside dans leur segmentation. Couplée à la classification, la segmentation permet une ségrégation sémantique de ces objets. Cependant, les méthodes existantes ne peuvent être considérées comme génériques, et bien que motivées par de nombreux domaines (militaire, médical, satellite, etc.), celles-ci sont continuellement réévaluées, adaptées et améliorées. Par exemple, les images satellites se démarquent dans le milieu de l'image de par leur spécificité d'acquisition, de par leur support ou de par le sujet d'observation (la Terre dans notre cas).Cette thèse à pour but d'explorer les méthodes de caractérisation et de segmentation supervisées exploitant la notion de texture. Les sols observés depuis l'espace, à des échelles et des résolutions différentes, peuvent être perçus comme texturés. Les cartes d'occupation des sols peuvent être obtenues par la segmentation d'images satellites, notamment en utilisant l'information texturale. Nous proposons le développement d'algorithmes de segmentation compétitifs caractérisant la texture par l'utilisation de représentations multi-échelles des images obtenues par décomposition en ondelettes et de classificateurs supervisés tels que les Support Vector Machines. Dans cette optique, cette thèse est principalement articulée autour de plusieurs projets de recherche nécessitant une étude des images à des échelles et des résolutions différentes, ces images étant elles-mêmes de nature variée (e.g. multi-spectrales, optiques, LiDAR). Nous dériverons, pour ces différents cas d'étude, certains aspects de la méthodologie développée.The analysis of digital images, albeit widely researched, continues to present a real challenge today. In the case of several applications which aim to produce an appropriate description and semantic recognition of image content, particular attention is required to be given to image analysis. In response to such requirements, image content analysis is carried out automatically with the help of computational methods that tend towards the domains of mathematics, statistics and physics. The use of image segmentation methods is a relevant and recognized way to represent objects observed in images. Coupled with classification, segmentation allows a semantic segregation of these objects. However, existing methods cannot be considered to be generic, and despite having been inspired by various domains (military, medical, satellite etc), they are continuously subject to reevaluation, adaptation or improvement. For example satellite images stand out in the image domain in terms of the specificity of their mode of acquisition, their format, or the object of observation (the Earth, in this case).The aim of the present thesis is to explore, by exploiting the notion of texture, methods of digital image characterization and supervised segmentation. Land, observed from space at different scales and resolutions, could be perceived as being textured. Land-use maps could be obtained through the segmentation of satellite images, in particular through the use of textural information. We propose to develop competitive algorithms of segmentation to characterize texture, using multi-scale representations of images obtained by wavelet decomposition and supervised classifiers such as Support Vector Machines.Given this context, the present thesis is principally articulated around various research projects which require the study of images at different scales and resolutions, and which are varying in nature (eg. multi-spectral, optic, LiDAR). Certain aspects of the methodology developed are applied to the different case studies undertaken

An automated method for tree-ring delineation based on active contours guided by DT-CWT complex coefficients in photographic images: Application to Abies alba wood slice images

International audienceThis paper describes an efficient method for delineating tree-rings and inter tree-rings in wood slice images. The method is based on an active contour approach and a multi-scale gradient map resulting from the Dual Tree Complex Wavelet Transform (DT-CWT). The method is automated and does not require any pith localization. It is also quite robust to some defect structures such as branch prints, cracks, knots or mold. We applied the method to process entire Abies alba wood slices (aged from 10 to 50 years) from bark to pith, which amounted to about 200 tree-rings. Our automatic delineation method performed accurately compared to the manual expert measurements with a mean F-score of 0.91 for the quality of delineation

Canopy height model characteristics derived from airbone laser scanning and its effectiveness in discriminating various tropical moist forest types

International audienceMapping tropical forests to a sufficient level of spatial resolution and structural detail is a prerequisite for their rational management, which however remains a largely unmet challenge. We explore the degree to which a forest canopy height model CHM derived from airborne laser scanning ALS can discriminate between five forest types of similar height but varying structure or composition. We systematically compare various textural features Haralick, Fourier transform-based, and wavelet-based features and various classification procedures linear discriminant analysis LDA, random forestRF, and support vector machine SVM applied to two sizes of sampling units 64 m × 64 m and 32 m × 32 m. Simple height distribution statistics achieve at best 70% classification accuracy in our sample set comprising 120 sampling units of 64 m × 64 m. Using w avelet-based features, this accuracy increases to 79% but drops by 10% with smaller sampling units 32 m × 32 m. Classifier performance depends on the texture feature set used, but SVM and RF tend to perform better than LDA. High discrimination rates between forests types of similar height indicate that the ALS-derived CHM provides information suitable for mapping of tropical forest types. Wavelet-based texture features coupled with a SVM classifier was found to be the most promising combination of methods. Ancillary data derived from laser scans and notably topography could be used jointly for an improved segmentation scheme

Examples of surrogates for images with increasing degree of autocorrelation.

<p>The first row features particular simulations of fractal patterns (fractional Brownian field) generated by Fourier synthesis <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0048766#pone.0048766-Keitt2" target="_blank">[27]</a> for four degrees of autocorrelation (from short- to long-ranged as indirectly quantified by the <i>β</i> parameter). The following rows (2 to 5) display one particular random realization (i.e. re-simulation) of each of these fractal patterns according to four surrogate producing methods (random reassignments, random shifts, iterative amplitude adjusted Fourier transform (IAAFT) and wavelet-based energy synthesis, respectively).</p

Type I error calibration curves for real data pertaining to earth relief or biomass production.

<p>Observed versus expected type I error probabilities resulting from an independence tests on 1000 pairs of simulated images, are plotted for every combination of method (colour curves) and dataset (panels). Methods compared are the corrected <i>t</i>-test of Dutilleul (ModT) and wavelet-based image synthesis method, with direction selectivity feature disabled (isotropic) and enabled (anisotropic). Data are non-overlapping windows extracted either from digital elevation models (SRTM 1.3°×1.3° windows) or net primary production map (NPP 2.3°×2.3° windows). The first row of the figure features particular extracts exemplifying the kind of patterns characteristic of each dataset. Each bin is 0.05 wide. The Kolmogorov-Smirnov (K.-S.) maximum difference statistic which measures the departure from the line of identity (dashed line) is indicated for each curve along with results of the derived test of the departure: *** = <i>p</i>-value<0.001; ** = 0.001≤<i>p</i>-value<0.01.</p

Type I error calibration curves for continuous fractal patterns generated through Fourier synthesis of 32×32 pixels images [27] (see Fig. 1 for examples).

<p>Observed versus expected type I error probabilities resulting from independence tests on 1000 pairs of simulated images, are plotted for every combination of method (colour curves) and degree of spatial autocorrelation (panels) as measured by the energy spectrum exponent, <i>β</i>. Methods compared are random reassignments, random shifts, corrected <i>t</i>-test of Dutilleul (ModT), iterative amplitude adjusted Fourier transform (IAAFT), and wavelet-based image synthesis. Each bin is 0.05 wide. The Kolmogorov-Smirnov (K.-S.) maximum difference statistic which measures the departure from the line of identity (dashed line) is indicated for each curve along with results of the derived test of the departure: *** = <i>p</i>-value<0.001; * = 0.01≤<i>p</i>-value<0.05; NS = not significant.</p