Image processing of multiphase images obtained via X-ray microtomography: A review

This is the publisher’s final pdf. The published article is copyrighted by the American Geophysical Union and can be found at: http://onlinelibrary.wiley.com/journal/10.1002/%28ISSN%291944-7973.Easier access to X-ray microtomography (lCT) facilities has provided much new insight from\ud high-resolution imaging for various problems in porous media research. Pore space analysis with respect to\ud functional properties usually requires segmentation of the intensity data into different classes. Image segmentation\ud is a nontrivial problem that may have a profound impact on all subsequent image analyses. This\ud review deals with two issues that are neglected in most of the recent studies on image segmentation: (i)\ud focus on multiclass segmentation and (ii) detailed descriptions as to why a specific method may fail\ud together with strategies for preventing the failure by applying suitable image enhancement prior to segmentation.\ud In this way, the presented algorithms become very robust and are less prone to operator bias.\ud Three different test images are examined: a synthetic image with ground-truth information, a synchrotron\ud image of precision beads with three different fluids residing in the pore space, and a lCT image of a soil\ud sample containing macropores, rocks, organic matter, and the soil matrix. Image blur is identified as the\ud major cause for poor segmentation results. Other impairments of the raw data like noise, ring artifacts, and\ud intensity variation can be removed with current image enhancement methods. Bayesian Markov random\ud field segmentation, watershed segmentation, and converging active contours are well suited for multiclass\ud segmentation, yet with different success to correct for partial volume effects and conserve small image features\ud simultaneously