BACKGROUND:Threat Image Projection (TIP) is a technique used in X-ray security baggage screening systems that superimposes a threat object signature onto a benign X-ray baggage image in a plausible and realistic manner. It has been shown to be highly effective in evaluating the ongoing performanceof human operators, improving their vigilance and performance on threat detection. OBJECTIVE:With the increasing use of 3D Computed Tomography (CT) in aviation security for both hold and cabin baggage screening a significant challenge arises in extending TIP to 3D CT volumes due to the difficulty in 3D CT volume segmentation and the proper insertion location determination. In this paper, we present an approach for 3D TIP in CT volumes targeting realistic and plausible threat object insertion within 3D CT baggage images. METHOD:The proposed approach consists of dual threat (source) and baggage (target) volume segmentation, particle swarm optimisation based insertion determination and metal artefact generation. In our experiments, real baggage data collected from airports are used to generate TIP volumes for evaluation. We also propose a TIP quality score metric to automatically estimate the quality of generated TIP volumes. RESULT:In our experiments with real baggage CT volumes and varying threat items, 90.25% of the generated TIP volumes are graded as good by human evaluation, 7% of them are of medium quality with minor flaws and 2.75% of them are bad. CONCLUSION:Qualitative evaluations on real 3D CT baggage imagery show that our approach is able to generate realistic and plausible TIP which are indiscernible from real CT volumes and the TIP quality scores are consistent with human evaluations.

中文翻译：

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3D X 射线计算机断层扫描体积内合理威胁图像投影 (TIP) 的参考架构。

背景：威胁图像投影 (TIP) 是 X 射线安全行李检查系统中使用的一种技术，它以合理且逼真的方式将威胁对象签名叠加到良性 X 射线行李图像上。它已被证明在评估人类操作员的持续表现、提高他们的警惕性和威胁检测性能方面非常有效。目标：随着 3D 计算机断层扫描 (CT) 在航空安全中越来越多地用于货舱和随身行李检查，由于 3D CT 体积分割和正确插入位置确定的困难，在将 TIP 扩展到 3D CT 体积方面出现了重大挑战。在本文中，我们提出了一种在 CT 体积中进行 3D TIP 的方法，其目标是在 3D CT 行李图像中插入真实可信的威胁对象。方法：所提出的方法包括双重威胁（源）和行李（目标）体积分割、基于粒子群优化的插入确定和金属制品生成。在我们的实验中，从机场收集的真实行李数据用于生成用于评估的 TIP 体积。我们还提出了一个 TIP 质量评分指标来自动估计生成的 TIP 卷的质量。结果：在我们对真实行李 CT 卷和不同威胁项目的实验中，90.25% 的生成 TIP 卷被人工评估评为良好，其中 7% 为中等质量，有轻微缺陷，2.75% 为差。结论：