Using a combined global and local scaling 3D segmentation method to calculate pore connectivity measures in CT soil images

2021 ◽  
Author(s):  
Juan José Martín Sotoca ◽  
Antonio Saa-Requejo ◽  
Sergio Zubelzu ◽  
Ana M. Tarquis
Keyword(s):  
Maximum Entropy ◽  
Maximum Entropy Method ◽  
Pore Space ◽  
Three Dimensional ◽  
Simulation Models ◽  
Entropy Method ◽  
Segmentation Method ◽  
Local Scaling ◽  
Structural Funds ◽  
Technological Advances

<p>The study of the spatial characteristics of soil pore networks is essential to obtain different parameters that will be useful in developing simulation models for a range of physical, chemical, and biological processes in soils. Over the last decade, major technological advances in X-ray computed tomography (CT) have allowed for the investigation and reconstruction of natural porous soils at very fine scales. Delimiting the pore network (pore space) from the CT soil images applying image binarization methods is a critical step. Different binarization methods can result in different spatial distributions of pores influencing the connectivity metrics used in the models.</p> <p>A combined global & local 2D segmentation method called “Combining Singularity-CA method” was successfully applied improving pore space detection. This method combines a local scaling method (Singularity-CA method) with a global one (Maximum Entropy method). The Singularity-CA method, based on fractal concepts, creates singularity maps, and the CA (Concentration Area) method is used to define local thresholds that can be applied to binarize CT soil images. Combining Singularity-CA (2D) method obtains better performance than the Singularity-CA and the Maximum Entropy method applied individually to the soil images.</p> <p>A new three dimensional binarization method is presented in this work. It combines the 3D Singularity-CV (Concentration Volume) method and a global one to improve 3D pore space detection. Porosity and connectivity metrics of soil pore spaces are calculated and compared to other segmentation methods.</p> <p> </p> <p>Acknowledgements:</p> <p>The authors acknowledge the support from Project No. PGC2018-093854-B-I00 of the "Ministerio de Ciencia, Innovación y Universidades" of Spain and the funding from the "Comunidad de Madrid" (Spain), Structural Funds 2014-2020 512 (ERDF and ESF), through project AGRISOST-CM S2018/BAA-4330.</p>

Combining global and local scaling (3D) methods to detect pore spaces in CT soil images

2021 ◽  
Author(s):  
Juan José Martin Sotoca ◽  
Antonio Saa Requejo ◽  
Sergio Zubelzu ◽  
Ana M. Tarquis
Keyword(s):  
Image Segmentation ◽  
Maximum Entropy ◽  
Maximum Entropy Method ◽  
Pore Space ◽  
Entropy Method ◽  
Local Scaling ◽  
Fractal Properties ◽  
Segmentation Methods ◽  
Soil Pore Space ◽  
Global And Local

<p>The characterization of the spatial distribution of soil pore structures is essential to obtain different parameters that will be useful in developing predictive models for a range of physical, chemical, and biological processes in soils. Over the last decade, major technological advances in X-ray computed tomography (CT) have allowed for the investigation and reconstruction of natural porous soils at very fine scales. Delimiting the pore structure (pore space) from the CT soil images applying image segmentation methods is crucial when attempting to extract complex pore space geometry information.</p><p>Different segmentation methods can result in different spatial distributions of pores influencing the parameters used in the models [1]. A new combined global & local segmentation (2D) method called “Combining Singularity-CA method” was successfully applied [2]. This method combines a local scaling method (Singularity-CA method) with a global one (Maximum Entropy method). The Singularity-CA method, based on fractal concepts, creates singularity maps, and the CA (Concentration Area) method is used to define local thresholds that can be applied to binarize CT images [3]. Comparing Singularity-CA method with classical methods, such as Otsu and Maximum Entropy, we observed that more pores can be detected mainly due to its ability to amplify anomalous concentrations. However, some small pores were detected incorrectly. Combining Singularity-CA (2D) method gives better pore detection performance than the Singularity-CA and the Maximum Entropy method applied individually to the images.</p><p>The Combining Singularity-CV (3D) method is presented in this work. It combines the Singularity – CV (Concentration Volume) method [4] and a global one to improve 3D pore space detection.</p><p> </p><p>References:</p><p>[1] Zhang, Y.J. (2001). A review of recent evaluation methods for image segmentation: International symposium on signal processing and its applications. Kuala Lumpur, Malaysia, 13–16, pp. 148–151.</p><p>[2] Martín-Sotoca, J.J., Saa-Requejo, A., Grau, J.B., Paz-González, A., and Tarquis, A.M. (2018). Combining global and local scaling methods to detect soil pore space. J. of Geo. Exploration, vol. 189, June 2018, pp 72-84.</p><p>[3] Martín-Sotoca, J.J., Saa-Requejo, A., Grau, J.B. and Tarquis, A.M. (2017). New segmentation method based on fractal properties using singularity maps. Geoderma, vol. 287, February 2017, pp 40-53. http://dx.doi.org/10.1016/j.geoderma.2016.09.005.</p><p>[4] Martín-Sotoca, J.J., Saa-Requejo, A., Grau, J.B. and Tarquis, A.M. (2018). Local 3D segmentation of soil pore space based on fractal properties using singularity maps. Geoderma, vol. 311, February 2018, pp 175-188. http://dx.doi.org/10.1016/j.geoderma.2016.11.029.</p><p> </p><p>Acknowledgements:</p><p>The authors acknowledge support from Project No. PGC2018-093854-B-I00 of the Spanish Ministerio de Ciencia Innovación y Universidades of Spain and the funding from the Comunidad de Madrid (Spain), Structural Funds 2014-2020 512 (ERDF and ESF), through project AGRISOST-CM S2018/BAA-4330.</p>

scholarly journals Research on Target Segmentation Method Based on Maximum Entropy Method

2019 ◽  
Vol 612 ◽  
pp. 032001
Author(s):  
Weiwei Gao ◽  
Hongyun Wang ◽  
Dan Fang ◽  
Yi Wang ◽  
Hongyan Zhang ◽  
...  
Keyword(s):  
Maximum Entropy ◽  
Maximum Entropy Method ◽  
Entropy Method ◽  
Segmentation Method ◽  
Target Segmentation

ENIGMA: maximum-entropy method program package for huge systems

2002 ◽  
Vol 35 (2) ◽  
pp. 282-286 ◽  
Author(s):  
Hiroshi Tanaka ◽  
Masaki Takata ◽  
Eiji Nishibori ◽  
Kenichi Kato ◽  
Takashi Iishi ◽  
...  
Keyword(s):  
Maximum Entropy ◽  
Message Passing ◽  
Message Passing Interface ◽  
Maximum Entropy Method ◽  
Computing Time ◽  
Three Dimensional ◽  
Entropy Method ◽  
Program Package ◽  
Fast Fourier Transformation ◽  
Memory Space

ENIGMA(Electron and Nuclear Image Generator by Max-ent Analysis) is a program package to evaluate three-dimensional electron and nuclear density from X-ray and neutron diffraction data by using the maximum-entropy method (MEM). Compared with the previous program packageMEED,ENIGMAsaves computing time and frees memory space at the same time by employing parallel data processing. The fast Fourier transformation (FFT) technique is also implemented. As a consequence of these improvements, the MEM analysis byENIGMAbecomes applicable to huge systems, such as proteins and polymers, when the phased structure factors are provided. The package is transferable to a wide variety of parallel computers, because it is written in Fortran 90 and a standard message-passing interface (MPI).

Enhanced Information Recovery in 2D On-and Off-Resonance Nutation NQR using the Maximum Entropy Method

1996 ◽  
Vol 51 (5-6) ◽  
pp. 337-347 ◽  
Author(s):  
Mariusz Maćkowiak ◽  
Piotr Kątowski
Keyword(s):  
Maximum Entropy ◽  
Maximum Entropy Method ◽  
Theoretical Description ◽  
Entropy Method ◽  
Zero Field ◽  
Resonance Effects ◽  
Information Recovery ◽  
Truncation Errors ◽  
Enhanced Resolution ◽  
2D Data

Abstract Two-dimensional zero-field nutation NQR spectroscopy has been used to determine the full quadrupolar tensor of spin - 3/2 nuclei in serveral molecular crystals containing the 3 5 Cl and 7 5 As nuclei. The problems of reconstructing 2D-nutation NQR spectra using conventional methods and the advantages of using implementation of the maximum entropy method (MEM) are analyzed. It is shown that the replacement of conventional Fourier transform by an alternative data processing by MEM in 2D NQR spectroscopy leads to sensitivity improvement, reduction of instrumental artefacts and truncation errors, shortened data acquisition times and suppression of noise, while at the same time increasing the resolution. The effects of off-resonance irradiation in nutation experiments are demonstrated both experimentally and theoretically. It is shown that off-resonance nutation spectroscopy is a useful extension of the conventional on-resonance experiments, thus facilitating the determination of asymmetry parameters in multiple spectrum. The theoretical description of the off-resonance effects in 2D nutation NQR spectroscopy is given, and general exact formulas for the asymmetry parameter are obtained. In off-resonance conditions, the resolution of the nutation NQR spectrum decreases with the spectrometer offset. However, an enhanced resolution can be achieved by using the maximum entropy method in 2D-data reconstruction.

Estimating the distribution of primary reflection coefficients

Geophysics ◽  
2003 ◽  
Vol 68 (4) ◽  
pp. 1417-1422 ◽  
Author(s):  
Danilo R. Velis
Keyword(s):  
Maximum Entropy ◽  
Maximum Entropy Method ◽  
Entropy Method ◽  
Reflection Coefficients ◽  
Log Data ◽  
Neuquen Basin ◽  
Neuquén Basin ◽  
Skewness And Kurtosis

The distribution of primary reflection coefficients can be estimated by means of the maximum entropy method, giving rise to smooth nonparametric functions which are consistent with the data. Instead of using classical moments (e.g. skewness and kurtosis) to constraint the maximization, nonconventional sample statistics help to improve the quality of the estimates. Results using real log data from various wells located in the Neuquen Basin (Argentina) show the effectiveness of the method to estimate both robust and consistent distributions that may be used to simulate realistic sequences.

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