Comparative visualization for parameter studies of dataset series

Muddassir Muhammad Malik, Christoph Heinzl, Eduard Gröller

Research output: Contribution to journalArticlepeer-review

39 Citations (Scopus)

Abstract

This paper proposes comparison and visualization techniques to carry out parameter studies for the special application area of dimensional measurement using 3D X-ray computed tomography (3DCT). A dataset series is generated by scanning a specimen multiple times by varying parameters of an industrial 3DCT device. A high-resolution series is explored using our planar-reformatting-based visualization system. We present a novel multi-image view and an edge explorer for comparing and visualizing gray values and edges of several datasets simultaneously. Visualization results and quantitative data are displayed side by side. Our technique is scalable and generic. It can be effective in various application areas like parameter studies of imaging modalities and dataset artifact detection. For fast data retrieval and convenient usability, we use bricking of the datasets and efficient data structures. We evaluate the applicability of the proposed techniques in collaboration with our company partners.

Original languageEnglish
Article number5401158
Pages (from-to)829-840
Number of pages12
JournalIEEE Transactions on Visualization and Computer Graphics (TVCG)
Volume16
Issue number5
DOIs
Publication statusPublished - 10 Jul 2010

Keywords

  • Comparative visualization
  • industrial computed tomography
  • multiple dataset visualization
  • parameter visualization
  • Computer Graphics
  • Humans
  • Tomography, X-Ray Computed/statistics & numerical data
  • Magnetic Resonance Imaging/statistics & numerical data
  • Imaging, Three-Dimensional/statistics & numerical data
  • Tomography, Emission-Computed, Single-Photon/statistics & numerical data
  • Radiographic Image Interpretation, Computer-Assisted
  • Databases, Factual

Fingerprint Dive into the research topics of 'Comparative visualization for parameter studies of dataset series'. Together they form a unique fingerprint.

Cite this