Porosity estimation method by X-ray computed tomography

Taud, Hind; Martinez-Angeles, Raymundo; Parrot, Jean-François; Hernandez-Escobedo, L.

doi:10.1016/j.petrol.2005.03.009

Cited by 159 publications

(97 citation statements)

References 33 publications

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“…The original method [1] was improved by the image processing in term of image noise and artifacts in order to be more complex. It is necessary to realize that the differences come from the following reasons: it is never the exactly same slice in both tomographic and metallographic methods; the resolution of tomograms and slices from optical devices considerably differs.…”

Section: Discussionmentioning

confidence: 99%

Porosity Measurement Method by X-Ray Computed Tomography

Prokop

Sveda

Jančárek

et al. 2009

KEM

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This paper describes a new non-destructive approach for porosity measurement developed in response to a need for a more accurate porosity measurement method for solid samples. The conventional methods in X-ray CT imaging are based on image segmentation where a threshold is applied at a user-defined value. As a result the uncertainty in the porosity measurement is introduced. Therefore, the new method, called the grey level method, which reflects the phenomenon of image processing and computation of ration between the volume of voids and the total volume of the entire sample, was considered and improved in term of image noise and artifacts. The volumes of 2D CT image as a surface are achieved by means of integrating the surface with operations relating to image histogram. The porosity value is given form the curve of a porosity distribution. Subsequently, the properties of individual pores were measured and comparison with conventional destructive method of porosity computation was carried out. Data analysis and image processing were realized in Matlab.

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Section: Discussionmentioning

confidence: 99%

Porosity Measurement Method by X-Ray Computed Tomography

Prokop

Sveda

Jančárek

et al. 2009

KEM

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“…Computed tomography (CT) scanning technology is a non-destructive method for obtaining the morphological information inside specimens (Iassonov et al 2009). The X-ray computed tomography imaging has been utilised for the porosity measurement of the rocks (Taud et al 2005). The application of CT scanning in the wood industry could be traced back to 1980s (Benson-Cooper 1982).…”

Section: Introductionmentioning

confidence: 99%

Porosity estimation of Phyllostachys edulis (Moso bamboo) by computed tomography and backscattered electron imaging

et al. 2016

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This study aims to investigate and quantify the porosity in the cross section of Phyllostachys edulis (Moso bamboo) culm wall. The porosity results are expected to be utilised in numerical study of heat and moisture transfer. Computed tomography (CT) and backscattered electron (BSE) imaging methods are utilised in this study because these two methods allow measurements of the anisotropic features of bamboo specimens. The results of these two methods can be represented as the function of the real dimension rather than the pore size distribution of the specimen. The specimens are obtained from eight different locations along the Moso bamboo culms. Both internodes and nodes specimens are measured in this study. The average porosity, standard deviation (SD) and coefficient of variation (COV) are calculated for BSE and CT results. Pearson product-moment correlation coefficient (PPMCC) is also calculated in this study to analyse the correlation between the BSE results and CT results. Typical porosity results from 400 sampling points and 10 portions average porosity are analysed in this study. The CT scanning results show similar trend with BSE results. The correlation relationship between BSE and CT results approaches moderate correlation level to strong correlation level. The average porosity of internode specimens is from 43.9 to 58.8 % by BSE measurement and from 44.9 to 63.4 % by CT measurement. The average porosity of node specimens is from 37.4 to 56.6 % by BSE measurement and from 32.1 to 62.2 % by CT measurement.

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“…This allows identification of the stochastic characteristics, microscopic and macroscopic parameters of the pore space and matrix structure, material constants, and their directional characteristics [8][9][10][11][12][13][14][15][16][17][18]. For this purpose, pure geometrical methods [9,14,[19][20][21] and methods of simulation of physical processes at microscopic level [12,13,16,18] are used.…”

Section: Introductionmentioning

confidence: 99%

Determination of bone porosity based on histograms of 3D μCT images

2014

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A new method is proposed for direct determination of bone porosity based on histograms of 3D lCT scans and for precise definition of the global image segmentation threshold, preserving assessed porosity in the reconstructed binary image of the bone sample. In this method, the normed histogram is considered to be a probability distribution of voxel density (CT number or gray level) in the scan. It is a linear combination of two distributions characterizing the frequency of occurrence of voxels of pore and matrix type with various densities. Volume porosity, in this model, defines the probability of pore voxel occurrence in the whole set of voxels in the scan of the sample. This parameter and the parameters of both probability distributions are determined by an optimization method. The new method was used to determine the porosity and segmentation thresholds for lCT images of two 3D samples of human cancellous bone. The results were compared with those determined by the standard method and Otsu's method. The new method allows the porosity and the image segmentation threshold to be determined even in cases where use of the other methods is questionable or impossible.

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Porosity estimation method by X-ray computed tomography

Cited by 159 publications

References 33 publications

Porosity Measurement Method by X-Ray Computed Tomography

Porosity Measurement Method by X-Ray Computed Tomography

Porosity estimation of Phyllostachys edulis (Moso bamboo) by computed tomography and backscattered electron imaging

Determination of bone porosity based on histograms of 3D μCT images

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