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Variational-based segmentation of bio-pores in tomographic images

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Authors

  • Benjamin Bauer
  • Xiaohao Cai
  • Stephan Peth
  • Katja Schladitz

External Research Organisations

  • Fraunhofer Institute for Industrial Mathematics (ITWM)
  • University of Cambridge
  • University of Kaiserslautern
  • University of Kassel
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Details

Original languageEnglish
Pages (from-to)1-8
Number of pages8
JournalComputers & geosciences
Volume98
Publication statusPublished - Jan 2017
Externally publishedYes

Abstract

X-ray computed tomography (CT) combined with a quantitative analysis of the resulting volume images is a fruitful technique in soil science. However the variations in X-ray attenuation due to different soil components keep the segmentation of single components within these highly heterogeneous samples a challenging problem. Particularly demanding are bio-pores due to their elongated shape and the low gray value difference to the surrounding soil structure. Recently variational models in connection with algorithms from convex optimization were successfully applied for image segmentation. In this paper we apply these methods for the first time for the segmentation of bio-pores in CT images of soil samples. We introduce a novel convex model which enforces smooth boundaries of bio-pores and takes the varying attenuation values in the depth into account. Segmentation results are reported for different real-world 3D data sets as well as for simulated data. These results are compared with two gray value thresholding methods, namely indicator kriging and a global thresholding procedure, and with a morphological approach. Pros and cons of the methods are assessed by considering geometric features of the segmented bio-pore systems. The variational approach features well-connected smooth pores while not detecting smaller or shallower pores. This is an advantage in cases where the main bio-pores network is of interest and where infillings, e.g., excrements of earthworms, would result in losing pore connections as observed for the other thresholding methods.

Keywords

    3D image segmentation, Bio-pores, Root system, Variational segmentation, Total variation minimization, Gray value thresholding, Morphological segmentation

ASJC Scopus subject areas

Cite this

Variational-based segmentation of bio-pores in tomographic images. / Bauer, Benjamin; Cai, Xiaohao; Peth, Stephan et al.
In: Computers & geosciences, Vol. 98, 01.2017, p. 1-8.

Research output: Contribution to journalArticleResearchpeer review

Bauer B, Cai X, Peth S, Schladitz K, Steidl G. Variational-based segmentation of bio-pores in tomographic images. Computers & geosciences. 2017 Jan;98:1-8. doi: 10.1016/j.cageo.2016.09.013
Bauer, Benjamin ; Cai, Xiaohao ; Peth, Stephan et al. / Variational-based segmentation of bio-pores in tomographic images. In: Computers & geosciences. 2017 ; Vol. 98. pp. 1-8.
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abstract = "X-ray computed tomography (CT) combined with a quantitative analysis of the resulting volume images is a fruitful technique in soil science. However the variations in X-ray attenuation due to different soil components keep the segmentation of single components within these highly heterogeneous samples a challenging problem. Particularly demanding are bio-pores due to their elongated shape and the low gray value difference to the surrounding soil structure. Recently variational models in connection with algorithms from convex optimization were successfully applied for image segmentation. In this paper we apply these methods for the first time for the segmentation of bio-pores in CT images of soil samples. We introduce a novel convex model which enforces smooth boundaries of bio-pores and takes the varying attenuation values in the depth into account. Segmentation results are reported for different real-world 3D data sets as well as for simulated data. These results are compared with two gray value thresholding methods, namely indicator kriging and a global thresholding procedure, and with a morphological approach. Pros and cons of the methods are assessed by considering geometric features of the segmented bio-pore systems. The variational approach features well-connected smooth pores while not detecting smaller or shallower pores. This is an advantage in cases where the main bio-pores network is of interest and where infillings, e.g., excrements of earthworms, would result in losing pore connections as observed for the other thresholding methods.",
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AU - Peth, Stephan

AU - Schladitz, Katja

AU - Steidl, Gabriele

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N2 - X-ray computed tomography (CT) combined with a quantitative analysis of the resulting volume images is a fruitful technique in soil science. However the variations in X-ray attenuation due to different soil components keep the segmentation of single components within these highly heterogeneous samples a challenging problem. Particularly demanding are bio-pores due to their elongated shape and the low gray value difference to the surrounding soil structure. Recently variational models in connection with algorithms from convex optimization were successfully applied for image segmentation. In this paper we apply these methods for the first time for the segmentation of bio-pores in CT images of soil samples. We introduce a novel convex model which enforces smooth boundaries of bio-pores and takes the varying attenuation values in the depth into account. Segmentation results are reported for different real-world 3D data sets as well as for simulated data. These results are compared with two gray value thresholding methods, namely indicator kriging and a global thresholding procedure, and with a morphological approach. Pros and cons of the methods are assessed by considering geometric features of the segmented bio-pore systems. The variational approach features well-connected smooth pores while not detecting smaller or shallower pores. This is an advantage in cases where the main bio-pores network is of interest and where infillings, e.g., excrements of earthworms, would result in losing pore connections as observed for the other thresholding methods.

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