Image analysis in nuclear forensics

In this technical report, a new approach for characterising powder of uranium ore concentrates (UOCs) is presented. It is based on image texture analysis and multivariate data modelling. Twenty-six different UOC samples were evaluated, applying the angle measure technique (AMT) algorithm to extract textural features on samples images acquired at 250× and 1 000× magnification by scanning electron microscope (SEM). At both magnifications, this method proved effective to classify the different types of UOC powder based on the surface characteristics that depend on particle size, homogeneity, and graininess and are related to the composition and processes used in the production facilities. Using the outcome data from the application of the AMT algorithm, the total explained variance was higher than 90 % with principal component analysis (PCA). This preliminary study shows that this method is able to distinguish samples with similar composition, but obtained from different facilities. The mean angle spectral data obtained by the image texture analysis using the AMT algorithm can be considered as a specific fingerprint or signature of UOCs and could be used for nuclear forensic investigation.