We introduce a novel method to automatically evaluate X-ray computed tomography (CT) images for the purpose of detecting material defects by evaluating the significance of features extracted by first order derivative filters. We estimate the noise of the original image and compute the noise of the filtered image via error propagation. The significance of these features can then be evaluated based on the signal-to-noise ratio in the filtered image. The major benefit of that procedure is, that a sample-independent threshold on the signal-to-noise ratio can be chosen. The results are demonstrated on parts drawn from an industrial manufacturing line. © Springer-Verlag Berlin Heidelberg 2002.
CITATION STYLE
Eisele, H., & Hamprecht, F. A. (2002). A new approach for defect detection in X-ray CT images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2449 LNCS, pp. 345–352). Springer Verlag. https://doi.org/10.1007/3-540-45783-6_42
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