  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, absence of a unified
benchmark prohibits quantitative evaluation, which further clouds the impact of
existing methodologies. In this study, we tackle the issue on both fronts.
Firstly, 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 while achieving a substantial reduction of the
computational complexity of the state-of-the-art competitors. Moreover,
statistical analysis reveals that the proposed method exhibits significant
robustness against the compositional variations of porous media.
