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Proceedings Paper

Unsupervised segmentation of soil x-ray microtomography images
Author(s): Ajay K. Mandava; Emma E. Regentova; Markus Berli
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Paper Abstract

Advances in X-ray microtomography (XMT) are opening new opportunities for examining soil structural properties and fluid distribution around living roots in-situ. The low contrast between moist soil, root and air-filled pores in XMT images presents a problem with respect to image segmentation. In this paper, we develop an unsupervised method for segmenting XMT images to pores (air and water), soil, and root regions. A feature-based segmentation method is provided to isolate regions that consist of similar texture patterns from an image based on the normalized inverse difference moment of gray-level co-occurrence matrix. The results obtained show that the combination of features, clustering, and post-processing techniques has advantageous over other advanced segmentation methods.

Paper Details

Date Published: 6 July 2015
PDF: 8 pages
Proc. SPIE 9631, Seventh International Conference on Digital Image Processing (ICDIP 2015), 96310Q (6 July 2015); doi: 10.1117/12.2197067
Show Author Affiliations
Ajay K. Mandava, Univ. of Nevada, Las Vegas (United States)
Emma E. Regentova, Univ. of Nevada, Las Vegas (United States)
Markus Berli, Desert Research Institute (United States)


Published in SPIE Proceedings Vol. 9631:
Seventh International Conference on Digital Image Processing (ICDIP 2015)
Charles M. Falco; Xudong Jiang, Editor(s)

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