Identification of pore spaces in 3D CT soil images using a PFCM partitional clustering

Ojeda Magaña, Benjamín and Quintanilla Domínguez, Joel and Tarquis Alfonso, Ana Maria and Tarquis Alfonso, Ana Maria and Gómez Barba, Leopoldo and Andina de la Fuente, Diego (2014). Identification of pore spaces in 3D CT soil images using a PFCM partitional clustering. "Geoderma", v. 217 ; pp. 90-101. ISSN 0016-7061. https://doi.org/10.1016/j.geoderma.2013.11.005.

Description

Title: Identification of pore spaces in 3D CT soil images using a PFCM partitional clustering
Author/s:
  • Ojeda Magaña, Benjamín
  • Quintanilla Domínguez, Joel
  • Tarquis Alfonso, Ana Maria
  • Tarquis Alfonso, Ana Maria
  • Gómez Barba, Leopoldo
  • Andina de la Fuente, Diego
Item Type: Article
Título de Revista/Publicación: Geoderma
Date: April 2014
Volume: 217
Subjects:
Freetext Keywords: Soil structure; Soil morphology; Image sub-segmentation; PFCM clustering
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Señales, Sistemas y Radiocomunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Recent advances in non-destructive imaging techniques, such as X-ray computed tomography (CT), make it possible to analyse pore space features from the direct visualisation from soil structures. A quantitative characterisation of the three-dimensional solid-pore architecture is important to understand soil mechanics, as they relate to the control of biological, chemical, and physical processes across scales. This analysis technique therefore offers an opportunity to better interpret soil strata, as new and relevant information can be obtained. In this work, we propose an approach to automatically identify the pore structure of a set of 200-2D images that represent slices of an original 3D CT image of a soil sample, which can be accomplished through non-linear enhancement of the pixel grey levels and an image segmentation based on a PFCM (Possibilistic Fuzzy C-Means) algorithm. Once the solids and pore spaces have been identified, the set of 200-2D images is then used to reconstruct an approximation of the soil sample by projecting only the pore spaces. This reconstruction shows the structure of the soil and its pores, which become more bounded, less bounded, or unbounded with changes in depth. If the soil sample image quality is sufficiently favourable in terms of contrast, noise and sharpness, the pore identification is less complicated, and the PFCM clustering algorithm can be used without additional processing; otherwise, images require pre-processing before using this algorithm. Promising results were obtained with four soil samples, the first of which was used to show the algorithm validity and the additional three were used to demonstrate the robustness of our proposal. The methodology we present here can better detect the solid soil and pore spaces on CT images, enabling the generation of better 2D?3D representations of pore structures from segmented 2D images.

More information

Item ID: 35661
DC Identifier: http://oa.upm.es/35661/
OAI Identifier: oai:oa.upm.es:35661
DOI: 10.1016/j.geoderma.2013.11.005
Official URL: http://www.sciencedirect.com/science/article/pii/S0016706113004126
Deposited by: Memoria Investigacion
Deposited on: 22 Jun 2015 16:26
Last Modified: 22 Jun 2015 16:26
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