X-ray Computed Tomography (CT) is an effective nondestructive technology used for security applications. In CT, three-dimensional images of the interior of an object are generated based on its X-ray attenuation. Multi-energy CT can be used to enhance material discrimination. Currently, reliable identification and segmentation of objects from CT data is challenging due to the large range of materials which may appear in baggage and the presence of metal and high clutter. Conventionally reconstructed CT images suffer from metal induced streaks and artifacts which can lead to breaking of objects and inaccurate object labeling. We propose a novel learning-based framework for joint metal artifact reduction and direct object labeling from CT derived data. A material label image is directly estimated from measured effective attenuation images. We include data weighting to mitigate metal artifacts and incorporate an object boundary-field to reduce object splitting. The overall problem is posed as a graph optimization problem and solved using an efficient graphcut algorithm. We test the method on real data and show that it can produce accurate material labels in the presence of metal and clutter.
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Limor Martin ; Ahmet Tuysuzoglu ; Prakash Ishwar and W. Clem Karl
Joint metal artifact reduction and material discrimination in X-ray CT using a learning-based graph-cut method
", Proc. SPIE 9020, Computational Imaging XII, 902007 (March 7, 2014); doi:10.1117/12.2048873; http://dx.doi.org/10.1117/12.2048873