Modelling mesoporous alumina microstructure with 3D random models of platelets.

This work focuses on a mesoporous material made up of nanometric alumina 'platelets' of unknown shape. We develope a 3D random microstructure to model the porous material, based on 2D transmission electron microscopy (TEM) images, without prior knowledge on the spatial distribution of alumina inside the material. The TEM images, acquired on samples with thickness 300 nm, a scale much larger than the platelets's size, are too blurry and noisy to allow one to distinguish platelets or platelets aggregates individually. In a first step, the TEM images correlation function and integral range are estimated. The presence of long-range fluctuations, due to the TEM inhomogeneous detection, is detected and corrected by filtering. The corrected correlation function is used as a morphological descriptor for the model. After testing a Boolean model of platelets, a two-scale model of microstructure is introduced to replicate the statistical dispersion of platelets observed on TEM images. Accordingly, a set of two-scale Boolean models with varying physically admissible platelets shapes is proposed. Upon optimization, the model takes into account the dispersion of platelets in the microstructure as observed on TEM images. Comparing it to X-ray diffraction and nitrogen porosimetry data, the model is found to be in good agreement with the material in terms of specific surface area.

Stereological reconstruction of polycrystalline materials.

Laguerre tessellations are suitable models for many polycrystalline materials. In this work, we present a reconstruction-based approach to fit a spatial Laguerre tessellation model to a plane section of a cellular material under the condition that one section of the model resembles the observed section of the material. To account for this special requirement, we introduce a novel Euclidean distance-based criterion for the model fitting. The model fitting itself is based on Simulated Annealing. If the structure under consideration is a Laguerre tessellation, we found a nearly perfect reconstruction of its spatial cell characteristics in the model. Even for a real sample of a sintered alumina the observed section is captured quite well by the model.

Direct estimation of austenitic grain dimensions in heat affected zones of a martensitic steel from EBSD images.

In the context of automated analyses of electron-backscattered-diffraction images, we present in this paper a novel method to automatically extract morphological properties of prior austenitic grains in martensitic steels based on raw crystallographic orientation maps. This quantification includes the estimation of the mean chord length in specific directions, with in addition the reconstruction of the mean shape of austenitic grains inducing anisotropic shape properties. The approach is based on the morphological measure of covariance on a decision curve of grain fidelity per disorientation angle. These efforts have been motivated by the need of realistic microstructures to perform micromechanical studies of grain boundary localized damage phenomenons in steels, one example being the type IV fracture phenomenon occurring in welded joints of grade P91/P92 steel. This failure is attributed to a change of the microstructure due to thermal gradients arising during the welding process. To precisely capture the relationships between microstructural changes and mechanical fields localization in a polycrystalline aggregate, we first need to achieve a reasonable stochastic model of its microstructure, which relies on a detailed knowledge of the microstructural morphology. As martensitic steels possess multiscale microstructures composed of prior austenitic grains, packets and laths, a relevant modelling strategy has to be proposed to account for the observed hierarchies. With this objective, this paper focuses on the larger scale entities present in the microstructure, namely, the austenitic grains.

Three-dimensional morphological modelling of concrete using multiscale Poisson polyhedra.

This paper aims at developing a random morphological model for concrete microstructures. A 3D image of concrete is obtained by microtomography and is used in conjunction with the concrete formulation to build and validate the model through morphological measurements. The morphological model is made up of two phases, corresponding to the matrix, or cement paste and to the aggregates. The set of aggregates in the sample is modelled as a combination of Poisson polyhedra of different scales. An algorithm is introduced to generate polyhedra packings in the continuum space. The latter is validated with morphological measurements.

Quantitative comparison of segmentation algorithms for FIB-SEM images of porous media.

Focused ion beam tomography has proven to be capable of imaging porous structures on a nano-scale. However, due to shine-through artefacts, common segmentation algorithms often lead to severe dislocation of individual structures in z-direction. Recently, a number of approaches have been developed, which take into account the specific nature of focused ion beam-scanning electron microscope images for porous media. In the present study, we analyse three of these approaches by comparing their performance based on simulated focused ion beam-scanning electron microscope images. Performance is measured by determining the amount of misclassified voxels as well as the fidelity of structural characteristics. Based on this analysis we conclude that each algorithm has certain strengths and weaknesses and we determine the scenarios for which each approach might be the best choice.

Morphological segmentation of FIB-SEM data of highly porous media.

Nanoporous materials play an important role in modern batteries as well as fuel cells. The materials microstructure needs to be analyzed as it determines the electrochemical properties. However, the microstructure is too fine to be resolved by microcomputed tomography. The method of choice to analyze the microstructure is focused ion beam nanotomography (FIB-SEM). However, the reconstruction of the porous 3D microstructure from FIB-SEM image data in general has been an unsolved problem so far. In this paper, we present a new method using morphological operations. First, features are extracted from the data. Subsequently, these features are combined to an initial segmentation, that is then refined by a constrained watershed transformation. We evaluate our method with synthetic data, generated by a simulation of the FIB-SEM imaging process. We compare the ground truth in the simulated data to the segmentation result. The new method is found to produce a much smaller error than existing techniques.

3D multiscale segmentation and morphological analysis of X-ray microtomography from cold-sprayed coatings.

X-ray microtomography from cold-sprayed coatings brings a new insight on this deposition process. A noise-tolerant segmentation algorithm is introduced, based on the combination of two segmentations: a deterministic multiscale segmentation and a stochastic segmentation. The stochastic approach uses random Poisson lines as markers. Results on a X-ray microtomographic image of aluminium particles are presented and validated.

Imaging and 3D morphological analysis of collagen fibrils.

The recent booming of multiphoton imaging of collagen fibrils by means of second harmonic generation microscopy generates the need for the development and automation of quantitative methods for image analysis. Standard approaches sequentially analyse two-dimensional (2D) slices to gain knowledge on the spatial arrangement and dimension of the fibrils, whereas the reconstructed three-dimensional (3D) image yields better information about these characteristics. In this work, a 3D analysis method is proposed for second harmonic generation images of collagen fibrils, based on a recently developed 3D fibre quantification method. This analysis uses operators from mathematical morphology. The fibril structure is scanned with a directional distance transform. Inertia moments of the directional distances yield the main fibre orientation, corresponding to the main inertia axis. The collaboration of directional distances and fibre orientation delivers a geometrical estimate of the fibre radius. The results include local maps as well as global distribution of orientation and radius of the fibrils over the 3D image. They also bring a segmentation of the image into foreground and background, as well as a classification of the foreground pixels into the preferred orientations. This accurate determination of the spatial arrangement of the fibrils within a 3D data set will be most relevant in biomedical applications. It brings the possibility to monitor remodelling of collagen tissues upon a variety of injuries and to guide tissues engineering because biomimetic 3D organizations and density are requested for better integration of implants.

TEM image analysis and modelling: application to boehmite nanoparticles.

Boehmite occurs in the form of nanoparticles. Upon drying, it can form the alumina that is common in catalyst support used in refining and petrochemicals. The topotactic transformation of boehmite alumina led to an interest in the precise shape and size of these nanoparticles which is highly linked to the catalyst activity. Boehmite nanoparticles can be observed by transmission electron microscopy. Although they are highly aggregated, the analysis of transmission electron microscopy images with a specific random model approach, here a dilution model, can give an accurate estimate of their size. To use this approach, electronic noise and diffraction artefacts on the edges of the nanoparticles have to be removed. Covariance measurements on micrographs can be performed. They can be used to fit a model. The fitting uses a novel numerical method to estimate the covariogram of grains. The model can take into account the specific orientations of the nanoparticles. The influence of noise, image filters used to remove noise and diffraction artefacts, as well as all the parameters of the model are all studied in this paper. We propose nanoparticle size estimations procedures based on both single and mixture-of-two particle models.

A multiscale microstructure model of carbon black distribution in rubber.

The increase of observations and computational capabilities favoured the numerical simulation of microstructure to derive the effective properties of materials. Indeed, the multiscale approaches, that use homogenization techniques, enable us to estimate or to give bounds of the overall properties of heterogeneous media. In this work, the objective is to develop a three-dimensional mathematical model of the morphology of the microstructure of rubber composite containing carbon black nano-fillers. This multiscale model consists of a combination of some primary models that correspond to the physical scales of the microstructure. It is identified according to an original method that uses statistical moments from experimental transmission electronic microscope (TEM) image data and from numerical TEM simulations. This method leads to three-dimensional representative simulations of microstructures that take the complex clustering effect of particles in aggregates, into account. Finally, the identified model of the morphology satisfies the experimental percolation rate of the carbon black aggregates in the material.

Texture classification by statistical learning from morphological image processing: application to metallic surfaces.

A classification method based on textural information for metallic surfaces displaying complex random patterns is proposed. Because these kinds of textures show fluctuations at a small scale and some uniformity at a larger scale, a probabilistic approach is followed, considering textural variations as realizations of random functions. Taking into account information of pixel neighbourhoods, the texture for each pixel is described at different scales. By means of statistical learning, the most relevant textural descriptors are selected for each application. The performance of this approach is established on a real data set of steel surfaces.

Segmentation of 3D microtomographic images of granular materials with the stochastic watershed.

Segmentation of 3D images of granular materials obtained by microtomography is not an easy task. Because of the conditions of acquisition and the nature of the media, the available images are not exploitable without a reliable method of extraction of the grains. The high connectivity in the medium, the disparity of the object's shape and the presence of image imperfections make classical segmentation methods (using image gradient and watershed constrained by markers) extremely difficult to perform efficiently. In this paper, we propose a non-parametric method using the stochastic watershed, allowing to estimate a 3D probability map of contours. Procedures allowing to extract final segmentation from this function are then presented.

A quantitative morphological analysis of nanostructured ceria-silica composite catalysts.

This study aims at examining the morphology of different catalysts, which are based on a dispersion of ceria nanoparticles embedded in a high surface area mesoporous silica framework. In order to fully describe the mesostructured composite material, we propose here a quantitative description of the microstructure based on a quantitative analysis of micrographs that were obtained via high-resolution transmission electron microscopy. We have therefore developed an automatic image analysis process in order to automatically and efficiently extract all the components of the catalyst images. A statistical and a morphological analysis of the spatial arrangement of the components of the catalyst are also presented. The study shows clear differences between the materials analysed in terms of the spatial arrangement and the total surface area of the ceria phase emerging into the pores, parameters of prime importance for the catalytic properties. Thus, the silica-ceria nanostructured composite materials, displaying large surface area up to 300 m(2) g(-1) are shown to exhibit highly rugged surfaces resulting from ceria nanoparticles emerging in the pores.

On the orientational analysis of planar fibre systems from digital images.

The orientational characteristics of fibres in digital images are studied. The fibres are modelled by a planar Boolean model whose typical grain is a thick (coloured) fibre. The aim is to make stereological inference on the rose of directions of the unobservable central fibres from observations made on a digital image of the thick fibres. For central fibres, the relation between the rose of directions and the point intensity, observed on a sampling line, is known. We derive, under regularity conditions, the relation between the unobservable point intensity and the scaled variogram observed on the line in a binary and a greyscale image. Using such a relation, it is possible to draw inference about the rose of directions from the scaled variogram, which is easy and quick to determine in a digital image.

Image analysis of insulation mineral fibres.

We present two methods for measuring the diameter and length of man-made vitreous fibres based on the automated image analysis of scanning electron microscopy images. The fibres we want to measure are used in materials such as glass wool, which in turn are used for thermal and acoustic insulation. The measurement of the diameters and lengths of these fibres is used by the glass wool industry for quality control purposes. To obtain reliable quality estimators, the measurement of several hundred images is necessary. These measurements are usually obtained manually by operators. Manual measurements, although reliable when performed by skilled operators, are slow due to the need for the operators to rest often to retain their ability to spot faint fibres on noisy backgrounds. Moreover, the task of measuring thousands of fibres every day, even with the help of semi-automated image analysis systems, is dull and repetitive. The need for an automated procedure which could replace manual measurements is quite real. For each of the two methods that we propose to accomplish this task, we present the sample preparation, the microscope setting and the image analysis algorithms used for the segmentation of the fibres and for their measurement. We also show how a statistical analysis of the results can alleviate most measurement biases, and how we can estimate the true distribution of fibre lengths by diameter class by measuring only the lengths of the fibres visible in the field of view.

Geostatistical and morphological methods applied to three-dimensional microscopy.

Three-dimensional (3-D) images of osteocyte lacunae were examined on a confocal microscope. Both geostatistical and morphological processing techniques were used to improve and to analyse them. By a geostastical approach, this study aims at improving 3-D confocal images before any further image processing. Optimized linear filters, which take account of the second-order statistics and the 3-D structure of the data, allow for the removal of imperfections such as noise and/or blur due to the axial convolution, and interpolate voxels on a face-centred cubic grid from an initial cubic grid. An application of this technique to 3-D biological images is demonstrated. In a second step, a 3-D binary image is digitized and cleaned with 3-D morphological filters. The standard 3-D measurements cannot be applied in this case, since all osteocytes cut the border of the field. For this reason a 3-D Boolean model has been adjusted, from which it is possible to derive all useful information on the repartition and the morphology of the osteocytes.

Homoeopathic treatment of cholera in Peru

The medical charity Homeopaths sans Frontieres sent a mission to a shanty town in Peru during the current cholera epidemic. Homoeopathic treatment was combined with fluid and electrolyte therapy. Results appeared favourable. A small-scale uncontrolled study is reported. Further studies are being undertaken

Mission against cholera in Peru

With enthusiasm and hard work, doctors from "Homoeopaths sans Frontiers" (HSF) have implemented the use of homoeopathy as an adjunct in the treatment of cholera during the epidemic in Peru. Under homoeopathic treatment recovery rate is rapidly improved. Apart from the help afforded to this badly hit population, the team are at the same time conducting a double-blind control study to assess the efficacy of the homoeopathic treatment. This is a great moment for homoeopathy - and may be the second time in history, when cholera epidemics lead to a break through in homoeopathy. Links is proud to be able to publish the first report of this project