  In this study, we introduce CobWeb 1.0 which is a graphical user interface
tailored explicitly for accurate image segmentation and representative
elementary volume analysis of digital rock images derived from high resolution
tomography. The CobWeb code is a work package deployed as a series of windows
executable binaries which use image processing and machine learning libraries
of MATLAB. The user-friendly interface enables image segmentation and
cross-validation employing K-means, Fuzzy C-means, least square support vector
machine, and ensemble classification (bragging and boosting) segmentation
techniques. A quick region of interest analysis including relative porosity
trends, pore size distribution, and volume fraction of different phases can be
performed on different geomaterials. Data can be exported to ParaView, DSI
Studio (.fib), Microsoft Excel and MATLAB for further visualisation and
statistical analysis. The efficiency of the new tool was verified using gas
hydrate-bearing sediment samples and Berea sandstone, both from synchrotron
tomography datasets, as well as Grosmont carbonate rock X-ray micro-tomographic
dataset. Despite its high sub-micrometer resolution, the gas hydrate dataset
was suffering from edge enhancement artefacts. These artefacts were primarily
normalized by the dual filtering approach using both non-local means and
anisotropic diffusion filtering. The desired automatic segmentation of the
phases (brine, sand, and gas hydrate) was thus successfully achieved using the
dual clustering approach.
