Geometrical approach for automatic detection of liquid surfaces in 3D computed tomography baggage imagery

Abstract

This study presents a novel method for liquid detection within three-dimensional (3D) computed tomography (CT) baggage inspection imagery. Liquid detection within airport security is currently of significant interest due to security threats associated with liquid explosives. In this paper, we propose a robust technique based on the automatic identification of universal geometric properties of liquids within 3D space. The proposed approach is based on two stages of geometric fitting. First, we identify the 3D plane which fits to the horizontally oriented surface of the liquid recognising the universal self-levelling property of liquids in any given container. Second, we conduct two-dimensional shape analysis to highlight the shape of the liquid surface at a given level within the container using a least squares elliptical fitting approach. The proposed approach relies on the fact that occurrences of such perfectly aligned horizontal planes within a 3D CT security baggage scan are generally unlikely. Occurrences of such instance are thus indicative of liquid presence. Our results, over an extended set of complex test examples, confirm a liquid detection rate of 85–98% with a moderate processing time. Furthermore, as this proposed approach is based purely on the geometric properties of liquids and robust geometrical shape detection, this methodology is intrinsic to the 3D nature of the resulting CT data and not dependent on any exemplar training imagery.