Unsupervised segmentation of soil x-ray microtomography images

Ajay K. Mandava; Emma E. Regentova; Markus Berli

doi:10.1117/12.2197067

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6 July 2015 Unsupervised segmentation of soil x-ray microtomography images

Ajay K. Mandava, Emma E. Regentova, Markus Berli

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Proceedings Volume 9631, Seventh International Conference on Digital Image Processing (ICDIP 2015); 96310Q (2015) https://doi.org/10.1117/12.2197067
Event: Seventh International Conference on Digital Image Processing (ICDIP15), 2015, Los Angeles, United States

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.

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Ajay K. Mandava, Emma E. Regentova, and Markus Berli "Unsupervised segmentation of soil x-ray microtomography images", Proc. SPIE 9631, Seventh International Conference on Digital Image Processing (ICDIP 2015), 96310Q (6 July 2015); https://doi.org/10.1117/12.2197067

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