Interactive Volume Exploration for Feature Detection and Quantification in Industrial CT Data

Interactive Volume Exploration for Feature Detection and Quantification in Industrial CT Data teaser image

This paper presents a novel method for interactive exploration of industrial CT volumes such as cast metal parts, with the goal of interactively detecting, classifying, and quantifying features using a visualization-driven approach. The standard approach for defect detection builds on region growing, which requires manually tuning parameters such as target ranges for density and size, variance, as well as the specification of seed points. If the results are not satisfactory, region growing must be performed again with different parameters. In contrast, our method allows interactive exploration of the parameter space, completely separated from region growing in an unattended pre-processing stage. The pre-computed *feature volume* tracks a *feature size curve* for each voxel over *time*, which is identified with the main region growing parameter such as variance. A novel 3D transfer function domain over *(density, feature size, time)* allows for interactive exploration of feature classes. Features and feature size curves can also be explored individually, which helps with transfer function specification and allows coloring individual features and disabling features resulting from CT artifacts. Based on the classification obtained through exploration, the classified features can be quantified immediately.

Resources

Citation

Markus Hadwiger, Laura Fritz, Christof Rezk-Salama, Thomas Höllt, Georg Geier, and Thomas Pabel. Interactive Volume Exploration for Feature Detection and Quantification in Industrial CT Data. IEEE Transactions on Visualization and Computer Graphics (Proceedings of IEEE SciVis), 14(6): pp. 1507–1514, 2008.

BibTeX

@article{ bib:2008_vis,
author = {Markus Hadwiger and Laura Fritz and Christof Rezk-Salama and Thomas H{\"o}llt and Georg Geier and Thomas Pabel},
title = { Interactive Volume Exploration for Feature Detection and Quantification in Industrial CT Data },
journal = { IEEE Transactions on Visualization and Computer Graphics (Proceedings of IEEE SciVis) },
number = { 6 },
pages = { 1507 -- 1514 },
year = { 2008 },
doi = { 10.1109/TVCG.2008.147 },
}