TopCap: an ImageJ plugin to automatically determine and quantify complex surface topologies and associated sub-surface structures in X-ray Computed Tomography images

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dc.contributor.authorGarbout, Amin
dc.contributor.authorSturrock, Craig
dc.contributor.authorArmenise, Elena
dc.contributor.authorAhn, Sujung
dc.contributor.authorSimmons, Robert W.
dc.contributor.authorDoerr, Stefan
dc.contributor.authorRitz, Karl
dc.contributor.authorMooney, Sacha
dc.date.accessioned2018-02-01T15:10:38Z
dc.date.available2018-02-01T15:10:38Z
dc.date.issued2017-10-20
dc.description.abstractThe surface of a material such as soil, as characterised by its topology and roughness, typically has a profound effect on its functional behaviour. Whilst non-destructive imaging techniques such as X-ray Computed Tomography (CT) have been extensively employed in recent years to characterise the internal architecture of soil, less attention has been paid to the morphology of the soil surface, possibly as other techniques such as scanning electron microscopy (SEM) and atomic force microscopy (AFM) are viewed as more appropriate. However, X-ray CT exploration of the surface of a soil also permits analysis immediately below its surface and beyond into the sample, contingent on its thickness. This provides important information such as how a connected structure might permit solute infiltration or gaseous diffusion through the surface and beyond into the subsurface matrix. A previous limitation to this approach had been the inability to segment and quantify the actual 3-D structural complexity at the surface, rather than a predefined geometrically simplistic volume immediately below it. To overcome this we formulated TopCap, a novel algorithm that operates with ImageJ as a plugin, which automatically captures the actual 3D surface morphology, segments the pore structure within the acquired 3D volume, and provides a series of incisive morphological measurements of the associated porous architecture. TopCap provides rapid, automated analysis of the immediate surface of materials and beyond, and whilst developed in the context of soil, is applicable to any 3D image volume.en_UK
dc.identifier.citationAmin Garbout; Craig Sturrock; Elena Armenise et. al., (2017) TopCap: an ImageJ plugin to automatically determine and quantify complex surface topologies and associated sub-surface structures in X-ray Computed Tomography images. Vadose Zone Journal, 2018, Vol. 17 No. 1, article number 170091en_UK
dc.identifier.issn1539-1663
dc.identifier.uri10.2136/vzj2017.05.0091
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/12943
dc.language.isoenen_UK
dc.publisherSoil Science Society of Americaen_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectsoil surfaceen_UK
dc.subjectsoil crusten_UK
dc.subjectX-ray Computed Tomographyen_UK
dc.subjectthresholden_UK
dc.subjectsurface detectionen_UK
dc.subjectporosityen_UK
dc.titleTopCap: an ImageJ plugin to automatically determine and quantify complex surface topologies and associated sub-surface structures in X-ray Computed Tomography imagesen_UK
dc.typeArticleen_UK

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