Quant-CT: Segmenting and Quantifying Computed Tomography

Abstract

Quant-CT is currently a plugin to ImageJ, designed as a Java-class that provides control mechanism for the user to choose volumes of interest within porous material, followed by the selection of image subsamples for automated tuning of parameters for filters and classifiers, and finally measurement of material geometry, porosity, and visualization. Denoising is mandatory before any image interpretation, and we implemented a new 3D java code that performs bilateral filtering of data. Segmentation of the dense material is essential before any quantifications about geological sample structure, and we invented new schemes to deal with over segmentation when using statistical region merging algorithm to pull out grains that compose imaged material. It make uses of ImageJ API and other standard and thirty-party APIs. Quant-CT conception started in 2011 under Scidac-e sponsor, and details of the first prototype were documented in publications below. While it is used right now for microtomography images, it can potentially be used by anybody with 3D image data obtained by experiment or produced by simulation.