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Proceedings Paper

Creation of an artifact database and experimental measurement of their detectability thresholds in noises of different spectra, in the context of quality control of an x-ray imager
Author(s): J.M. Vignolle; L. Debize; I. Bensaid; R. Forich; C. Berthaud; C. Marmajou; B. Candiard
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Paper Abstract

In order to assess the quality of an X-ray imager it is necessary to measure the visibility of any artifact that might be present in the image. Several methods have been proposed in the literature to calculate this visibility. To predict the performance of these methods in the context of quality control of X-ray imagers, a base of 10 artifacts as different as possible in shape and aspect have been created (a pixel, a line, a step, various spots and noises). The amplitude for which each artifact has a probability of 50% to be detected has been determined. To do so, the artifacts have been observed merged with three noises of different spectra ("white noise", "high-frequency" noise and "low-frequency" noise). To determine the 50% detection probability amplitudes, a variant of the 2 Alternative Forced Choice procedure has been used. It has been checked that the measurement exploitation method is unbiased and its precision is sufficient. The dispersion of results between testers, around 15% in average, is also satisfactory. These results are a solid and objective basis to check the relevance and limits of visibility measurement methods described in literature, applied to the domain of quality control of X-ray imagers.

Paper Details

Date Published: 12 July 2011
PDF: 11 pages
Proc. SPIE 8000, Tenth International Conference on Quality Control by Artificial Vision, 80000B (12 July 2011); doi: 10.1117/12.888838
Show Author Affiliations
J.M. Vignolle, Trixell (France)
L. Debize, Trixell (France)
I. Bensaid, Trixell (France)
R. Forich, Trixell (France)
C. Berthaud, Trixell (France)
C. Marmajou, Trixell (France)
B. Candiard, Trixell (France)

Published in SPIE Proceedings Vol. 8000:
Tenth International Conference on Quality Control by Artificial Vision
Jean-Charles Pinoli; Johan Debayle; Yann Gavet; Frédéric Gruy; Claude Lambert, Editor(s)

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