3D Quantum Cuts for automatic segmentation of porous media in tomography images. (February 2022)

• Record Type: Journal Article
• Authors: Malik, Junaid; Kiranyaz, Serkan; Al-Raoush, Riyadh I.; Monga, Olivier; Garnier, Patricia; Foufou, Sebti; Bouras, Abdelaziz; Iosifidis, Alexandros; Gabbouj, Moncef; Baveye, Philippe C.
• Is Part Of: Computers & geosciences. Volume 159(2022)
• Publication Date: 2022-02
• Subjects: Computed micro-tomography -- Soil segmentation -- Porous media -- Graph cuts; Environmental policy -- Periodicals; 550.5
• DOI: 10.1016/j.cageo.2021.105017
• Languages: English
• ISSNs: 0098-3004

Abstract

Abstract: Binary segmentation of volumetric images of porous media is a crucial step towards gaining a deeper understanding of the factors governing biogeochemical processes at minute scales. Contemporary work primarily revolves around primitive techniques based on global or local adaptive thresholding that have known common drawbacks in image segmentation. Moreover, the absence of a unified benchmark prohibits quantitative evaluation, which further undermines the impact of existing methodologies. In this study, we tackle the issue on both fronts. First, by drawing parallels with natural image segmentation, we propose a novel, and automatic segmentation technique, 3D Quantum Cuts (QCuts-3D) grounded on a state-of-the-art spectral clustering technique. Secondly, we curate and present a publicly available dataset of 68 multiphase volumetric images of porous media with diverse solid geometries, along with voxel-wise ground truth annotations for each constituting phase. We provide comparative evaluations between QCuts-3D and the current state-of-the-art over this dataset across a variety of evaluation metrics. The proposed systematic approach achieves a 26% increase in AUROC (Area Under Receiver Operating Characteristics) while achieving a substantial reduction of the computational complexity over state-of-the-art competitors. Moreover, statistical analysis reveals that the proposed method exhibits significant robustness against the compositional variations of porous media. Highlights: A novel unsupervised graph-cut based segmentation technique is presented for binarization of porous media tomography images. A benchmark dataset of volumetric tomography images with diverse grain geometry is provided. Local window-based segmentation is inadequate for accurately capturing regional intricacies in complex multi-phase systems. Proposed approach is more robust to variations in granular media characteristics as compared to existing state-of-the-art.