CobWeb 1.0: Machine Learning Tool Box for Tomographic Imaging

Abstract. Despite the availability of both commercial and open source software, an ideal tool for digital rock physics analysis for accurate automatic image analysis at ambient computational performance is difficult to pin point. More often image segmentation is driven manually where the performance remains limited to two phases. Discrepancies due to artefacts causes inaccuracies in image analysis. To overcome these problems, we have developed CobWeb 1.0 which is automated and explicitly tailored for accurate grayscale (multi-phase) image segmentation using unsupervised and supervised machine learning techniques. The simple and intuitive layout of the graphical user interface enables easy access to perform Image enhancement, Image segmentation and further to obtain the accuracy of different segmented classes. The graphical user interface enables not only processing of a full 3D digital rock dataset but also provides a quick and easy region-of-interest selection, where a representative elementary volume can be extracted and processed. The CobWeb software package covers image processing and machine learning libraries of MATLAB ® used for image enhancement and image segmentation operations, which are compiled into series of windows executable binaries. Segmentation can be performed using unsupervised, supervised and ensemble classification tools. Additionally, based on the segmented phases, geometrical parameters such as pore size distribution, relative porosity trends and volume fraction can be calculated and visualized. The CobWeb software allows the export of data to various formats such as ParaView (.vtk), DSI Studio (.fib) for visualization and animation and Microsoft ® Excel and MATLAB ® for numerical calculation and simulations. The capability of this new software is verified using high-resolution synchrotron tomography datasets, as well as lab-based (cone-beam) X-ray micro-tomography datasets. Albeit the high spatial resolution (sub-micrometer), the synchrotron dataset contained edge enhancement artefacts which were eliminated using a novel dual filtering and dual segmentation procedure.