Automated detection of artefacts for computed tomography in dimensional metrology

Conference contribution


Publication Details

Author(s): Fleßner M, Müller A, Helmecke E, Hausotte T
Publication year: 2015
Conference Proceedings Title: Digital Industrial Radiology and Computed Tomography


Abstract


Artefacts within CT volume data have a large impact on the results of dimensional measurements. To avoid measurement deviations, it is therefore crucial to identify regions affected by artefacts. The presented method analyses the volume data in the proximity of an extracted surface point to calculate a Local Quality Value (LQV). Using this method, surface points affected by artefacts are identified and highlighted in 2D and 3D visualisations. As only the volume data and the extracted surface are required to calculate the LQV, no additional knowledge like a CAD model or a reference measurement is necessary and the analysis can be carried out automatically. CT scans of calibrated gauges blocks that exhibit large errors in the segmented surface dataset due to artefacts are used to demonstrate the capability of the presented method. It is shown that it is possible to increase the accuracy of dimensional measurements by considering the information provided by the LQV.



FAU Authors / FAU Editors

Fleßner, Matthias
Lehrstuhl für Fertigungsmesstechnik
Hausotte, Tino Prof. Dr.-Ing.
Lehrstuhl für Fertigungsmesstechnik
Helmecke, Eric
Lehrstuhl für Fertigungsmesstechnik
Müller, Andreas
Lehrstuhl für Fertigungsmesstechnik


How to cite

APA:
Fleßner, M., Müller, A., Helmecke, E., & Hausotte, T. (2015). Automated detection of artefacts for computed tomography in dimensional metrology. In Digital Industrial Radiology and Computed Tomography. Gent, BE.

MLA:
Fleßner, Matthias, et al. "Automated detection of artefacts for computed tomography in dimensional metrology." Proceedings of the International Symposium on Digital Industrial Radiology and Computed Tomography (DIR 2015), Gent 2015.

BibTeX: 

Last updated on 2018-19-04 at 03:04