Interactive volume exploration for feature detection and quantification in industrial CT data

Markus Hadwiger*, Laura Fritz, Christof Rezk-Salama, Thomas Höllt, Georg Geier, Thomas Pabel

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

46 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number4658169
Pages (from-to)1507-1514
Number of pages8
JournalIEEE Transactions on Visualization and Computer Graphics
Volume14
Issue number6
DOIs
StatePublished - Nov 1 2008

Keywords

  • Multi-dimensional transfer functions
  • Non-destructive testing
  • Region growing
  • Volume rendering

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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