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Marshall, Stephen
University of Strathclyde
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (12/12 displayed)
- 2023Passive gamma-ray analysis of UO2 fuel rods using SrI2(Eu) scintillators in multi-detector arrangements
- 2022X-ray classification of Special Nuclear Materials using image segmentation and feature descriptors
- 2017Automated microstructural analysis of titanium alloys using digital image processingcitations
- 2016Use of hyperspectral imaging for artwork authentication
- 2015Detection and characterisation of the solar UV network
- 2015Automated image stitching for fuel channel inspection of AGR cores
- 2013Automated image stitching for enhanced visual inspections of nuclear power stations
- 2012A review of recent advances in the hit-or-miss transformcitations
- 2011A fast method for computing the output of rank order filters within arbitrarily shaped windows
- 2007Restoration of star-field images using high-level languages and core libraries
- 2006Advances in nonlinear signal and image processing
- 2005Texture classification of grey scale corrosion images
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document
X-ray classification of Special Nuclear Materials using image segmentation and feature descriptors
Abstract
Reliable inspection techniques are crucial for the safe storage and transport of nuclear materials. Among the factors to be considered is the morphology of Special Nuclear Materials, typically stored in packages of multiple layered cannisters. X-ray radiography allows visual inspection of the material inside, without risking exposure. However, some morphologies of material have visual similarities which risks errors being made when determining package contents from radiographs. Image processing techniques can automate the classification of radiographs in a deterministic way, thus providing a valuable inspection aid to nuclear storage facilities. In this paper, segmentation methods are proposed to identify the nuclear materials inside the package, and feature extraction methods are designed that derive multiple descriptors of the shape and morphology of the segmented material. Machine learning is then used to train a model that uses only the extracted feature descriptors to classify radiographs into 3 different morphologies; powder, pellets and clinker. This technique is tested on 138 X-ray images and initial results are very promising.