Exploring Large Quantities of Secondary Data from High-Resolution Synchrotron X-ray Computed Tomography Scans Using AccuStripes

TL;DR

The paper addresses the visualization challenge of large-scale secondary data derived from high-resolution synchrotron X-ray CT scans by introducing AccuStripes, a visualization approach that represents distributions of per-feature properties (e.g., volume, sphericity) across spatial tiles as ensembles of histograms. It combines three binning strategies—Uniform (UB), Bayesian Blocks (BB), and Jenks' Natural Breaks (NB)—with three composition modes—color-only, overlay, and filled curve—to flexibly reveal distributional structure and enable comparisons across many regions. The methodology includes an end-to-end pipeline: SCT imaging, interactive machine-learning segmentation, 54-tile partitioning, and per-particle quantification stored in CSV files, followed by distribution visualization with AccuStripes. Application to a particle-reinforced AlSiMMC demonstrates how adaptive binning improves interpretability over uniform binning, enabling insights into particle size distributions and spatial variation, while recognizing limitations in linking distributions to 3D spatial context and scalability to even larger datasets; future work proposes 3D linkage, clustering, and progressive analytics to extend applicability.

Abstract

The analysis of secondary quantitative data extracted from high-resolution synchrotron X-ray computed tomography scans represents a significant challenge for users. While a number of methods have been introduced for processing large three-dimensional images in order to generate secondary data, there are only a few techniques available for simple and intuitive visualization of such data in their entirety. This work employs the AccuStripes visualization technique for that purpose, which enables the visual analysis of secondary data represented by an ensemble of univariate distributions. It supports different schemes for adaptive histogram binnings in combination with several ways of rendering aggregated data and it allows the interactive selection of optimal visual representations depending on the data and the use case. We demonstrate the usability of AccuStripes on a high-resolution synchrotron scan of a particle-reinforced metal matrix composite sample, containing more than 20 million particles. Through AccuStripes, detailed insights are facilitated into distributions of derived particle characteristics of the entire sample. Furthermore, research questions such as how the overall shape of the particles is or how homogeneously they are distributed across the sample can be answered.

Figures (12)

• Figure 1: Analysis workflow for SCT scans: Primary data is generated via SCT imaging. Secondary data is derived from the primary data by applying segmentation and quantification processing. The resulting secondary data is then visualized using various AccuStripes visualizations
• Figure 2: AccuStripes: The Gaussian distribution, shown in the line chart, is visualized by all nine AccuStripes representations. The three compositions are shown from top to bottom; the three possible binning methods are shown from left to right
• Figure 3: A XY slice of the metal matrix composite considered in this work. The zoomed region shows the contained SiC particles (white) within the metal matrix (gray) and several pores
• Figure 4: The slice from Fig. \ref{['fig:mmcslice']} of the segmentation result. The particles are well segmented and many of them are easily separable in 3D, except for agglomerations around pores
• Figure 5: Histogram of the particles' volume showing that most particles have a small volume, while identifying the presence of outliers