三维量子切割在层析图像多孔介质自动分割中的应用
3D Quantum Cuts for Automatic Segmentation of Porous Media in Tomography Images
摘要：
多孔介质体积图像的二值分割是深入了解微尺度下地球化学过程的关键一步。当代的工作主要围绕原始技术的基础上，全球或当地的自适应阈值，在图像分割中有已知的共同缺点。此外，由于缺乏统一的基准，无法进行定量评价，从而进一步模糊了现有方法的影响。在这项研究中，我们从两个方面解决这个问题。首先，通过绘制平行自然图像分割，我们提出了一种新的，自动分割技术，3D量子切割（QCuts-3D）接地上一个国家的最先进的谱聚类技术。其次，我们策划并提出了一个公开的数据集的68多相体积图像的多孔介质与不同的固体几何形状，随着体素明智的地面实况注释为每个构成阶段。我们提供了QCuts-3D和当前最先进的数据集之间的比较评估，包括各种评估指标。所提出的系统方法实现了26%的AUROC增加，同时实现了最先进的竞争对手的计算复杂性的大幅降低。此外，统计分析表明，该方法具有显着的鲁棒性对多孔介质的组成变化。
英文摘要：
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.
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