Pores in Spherical Radiolarian Skeletons Directly Determined from Three-Dimensional Data (original) (raw)
Related papers
We investigated two geometrical models of skeleton forms of spherical radiolaria. Both models are based on Voronoi tessellation of given points on a sphere. We allocate a given number of points called " generators " , which can be related to pore frames, and obtained their Voronoi tessellation and approximated polyhedron. The first model is based on random allocation of generators, and the second one is based on global minimization of a potential function whose value is calculated from a generator distribution. Depending on the types of these generator distributions, we obtained different types of approximated polyhedrons. Using these polyhedrons, we analyzed the frequency distributions of the number of vertices of the polygons and the total edge lengths. We then compared the polyhedrons derived by the tessellation with real radiolaria. A comparison of frequency distributions revealed that the first model is not sufficient for mesh-like radiolaria. However, the second mode...
Statistical segmentation and porosity quantification of 3D x-ray microtomography
Applications of Digital Image Processing XXXIV, 2011
High-resolution x-ray micro-tomography is used for imaging of solid materials at micrometer scale in 3D. Our goal is to implement nondestructive techniques to quantify properties in the interior of solid objects, including information on their 3D geometries, which supports modeling of the fluid dynamics into the pore space of the host object. The micro-tomography data acquisition process generates large data sets that are often difficult to handle with adequate performance when using current standard computing and image processing algorithms. We propose an efficient set of algorithms to filter, segment and extract features from stacks of image slices of porous media. The first step tunes scale parameters to the filtering algorithm, then it reduces artifacts using a fast anisotropic filter applied to the image stack, which smoothes homogeneous regions while preserving borders. Next, the volume is partitioned using statistical region merging, exploiting the intensity similarities of each segment. Finally, we calculate the porosity of the material based on the solid-void ratio. Our contribution is to design a pipeline tailored to deal with large data-files, including a scheme for the user to input image patches for tuning parameters to the datasets. We illustrate our methodology using more than 2,000 micro-tomography image slices from 4 different porous materials, acquired using high-resolution X-ray. Also, we compare our results with standard, yet fast algorithms often used for image segmentation, which includes median filtering and thresholding.
3D reconstruction and quantification of porous structures
Computers & Graphics, 2008
In this paper, we describe the methodology that we have designed to quantify the pores distribution in bone implants and the empirical results that we have obtained with BioCAD designed scaffolds, microCT and confocal microscopy data. Our method is based on 3D digital topology properties of the porous structure. We segment the 3D images into three regions: exterior, bone and pore space. Next, we divide the pore space into pores and connection paths. We compute a graph of the pore space such that each node of the graph represents a pore, and an arc between two nodes indicates that the two pores are path-connected. On the basis of the graph and the segmented model, we are able to compute several properties of the material such as global porosity, effective porosity and radial pore distribution.
Chemical Engineering Science, 2016
Micro-packed beds (µPBs) are seeing increasing use in the process intensification context (e.g. micro-reactors), in separation and purification, particularly in the pharmaceutical and bio-products sectors, and in analytical chemistry. The structure of the stationary phase and of the void space it defines in such columns is of interest because it strongly influences performance. However, instrumental limitations-in particular the limited resolution of various imaging techniques relative to the particle and void space dimensions-have impeded experimental study of the structure of µPBs. We report here a new method that obviates this issue when the µPBs are composed of particles that may be approximated by monodisperse spheres. It achieves this by identifying in successive cross-sectional images of the bed the approximate centre and diameter of the particle cross-sections, replacing them with circles, and then assembling them to form the particles by identifying correlations between the successive images. Two important novel aspects of the method proposed here are it does not require specification of a threshold for binarizing the images, and it preserves the underlying spherical geometry of the packing. The new method is demonstrated through its application to a packing of a near-monodispersed 30.5 µm particles of high sphericity within a 200 µm square cross-section column imaged using a machine capable of 2.28 µm resolution. The porosity obtained was, within statistical uncertainty, the same as that determined via a direct method whilst use of a commonly used automatic thresholding technique yielded a result that was nearly 10% adrift, well beyond the experimental uncertainty. Extension of the method to packings of spherical particles that are less monodisperse or of different regular shapes (e.g. ellipsoids) is also discussed.
Statistical segmentation and porosity quantification of 3D x-ray microtomography
2011
abstract High-resolution x-ray micro-tomography is used for imaging of solid materials at micrometer scale in 3D. Our goal is to implement nondestructive techniques to quantify properties in the interior of solid objects, including information on their 3D geometries, which supports modeling of the fluid dynamics into the pore space of the host object.
A connected-component-labeling-based approach to virtual porosimetry
Graphical Models, 2011
Analyzing the pore-size distribution of porous materials, made up of an aggregation of interconnected pores, is a demanding task. Mercury intrusion porosimetry (MIP) is a physical method that intrudes mercury into a sample at increasing pressures to obtain a poresize histogram. This method has been simulated in-silice with several approaches requiring prior computation of a skeleton. We present a new approach to simulate MIP that does not require skeleton computation. Our method is an iterative process that considers the diameters corresponding to pressures. At each iteration, geometric tests detect throats for the corresponding diameter and a CCL process collects the region invaded by the mercury. Additionally, a new decomposition model called CUDB, is used. This is suitable for computing the throats and performs better with the CCL algorithm than a voxel model. Our approach obtains the pore-size distribution of the porous medium, and the corresponding pore graph.
Mathematics and Computers in Simulation, 2014
Different image processing techniques have recently been investigated for the characterization of complex porous media, such as bones, stones and soils. Among these techniques, 3D thinning algorithms are generally used to extract a one-voxel-thick skeleton from 3D porous objects while preserving the topological information. Models based on simplified skeletons have been shown to be efficient in retrieving morphological information from large scale disordered objects not only at a global level but also at a local level. In this paper, we present a series of 3D skeleton-based image processing techniques for evaluating the micro-architecture of large scale disordered porous media. The proposed skeleton method combines curve and surface thinning methods with the help of an enhanced shape classification algorithm. Results on two different porous objects demonstrate the ability of the proposed method to provide significant topological and morphological information. A. Almhdie et al. / Mathematics and Computers in Simulation 99 (2014) 82-94 83 determining the main characteristics (morphology, texture, topology, etc.) of these porous media. Furthermore, the prediction of properties (e.g. transport or mechanical properties) by models and simulations needs a realistic description of the phases constituting these porous materials.
Medial axis analysis of void structure in three-dimensional tomographic images of porous media
Journal of Geophysical Research: Solid Earth, 1996
We introduce the medial axis as a tool in the analysis of geometric structure of void space in porous media. The medial axis traces the fundamental geometry of the void pathways. We describe an algorithm for generating the medial axis of the void structure from digitized three dimensional images of porous media obtained from X ray CAT scans. The medial axis is constructed during an iterative erosion procedure which, at each step, replaces the image of the void structure with a smaller version obtained by eroding its surface layer of voxels. The algorithm is applied to high (5 m) resolution microtomographic images of two rock chips (Berea sandstone and Danish chalk) and a sample of uniform (100 m) diameter, packed glass beads. We statistically investigate several geometrical properties of the structure of the medial axes obtained. The rst is the distribution of relative volumes in each erosion layer of the void space. We nd the distributions to be exponential for the two real rock samples and normal for the packed glass beads. The second property investigated is the distribution of volumes of disconnected segments of the medial axis which are in one-to-one correspondence with disconnected void segments of the sample. We nd indications for a universal power law behavior governing the distribution of volumes of the smallest disconnected pieces. The nal behavior studied is a geometric tortuosity as measured by shortest paths through the medial axis. This tortuosity distribution appears well described by a gamma distribution.
e-Journal of Nondestructive Testing, 2022
The fourth industrial revolution has brought many benefits to the mechanical engineering world. Through data revolution, defect segmentation in complex XCT images can now be automated in a shorter time while achieving more accurate results. Prior work [1] proves that a deep learning approach can extract pores from every voxel of 3D XCT data and calculate its porosity with high accuracy. However, it was established that training a deep learning model with limited data can cause the model to overfit or have inferior segmentation performance than models that are trained with a larger dataset. This overfit issue is a common issue in all sorts of data-driven inspections. Often, obtaining raw data and annotating these raw data for machine learning inspection can take significant resources, shifting the focus away from the network design and training. We present XCTPore, an open source database of X-ray CT images containing 2D slices and 3D volumetric data of X-ray CT scanned additively manufactured components with varying porosity and image quality. This database is currently maintained by the Advanced Remanufacturing and Technology Centre (ARTC) and is open to industry practitioners and academic contributors. The content of the database can be queried through our python code found in our GitHub link 1 .
2018 14th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), 2018
We work with tomographic images of pore space in soil. The images have large dimensions and so in order to speed-up biological simulations (as drainage or diffusion process in soil), we want to describe the pore space with a number of geometrical primitives significantly smaller than the number of voxels in pore space. In this paper, we use the curve skeleton of a volume to segment it into some regions. We describe the method to compute the curve skeleton and to segment it with a simple segment approximation. We approximate each obtained region with an ellipsoid. The set of final ellipsoids represents the geometry of pore space and will be used in future simulations. We compare this method which we call geometrical method with the one described in the paper [8], which we name statistical method (using k-means algorithm).