Computationally Efficient Simulations Segmentation and Image Reconstruction for CT X-ray Based EDS Systems Screening
F3-D (Phase 1)

Download the 2013 Project Report

Despite recent technological advances, reliable detection of explosives in luggage and IEDs remains a challenging problem. For explosives detection in checked luggage, X-ray computed tomography (CT) is a widely-adapted sensing modality. CT X-ray based explosive detection algorithms are comprised of image reconstruction, segmentation and classification steps where first, three-dimensional X-ray attenuation images are formed. Next, the content of the images are decomposed into homogenous connected regions. Finally, the connected components are identified. Typically the image formation and segmentation are computationally the most intensive steps of the Explosive Detection System (EDS) image processing chain. This research aims to develop computationally efficient simultaneous image reconstruction and segmentation algorithms.

In particular, we develop:

  1. analytic forward models for conebeam X-ray CT that can incorporate realistic system parameters, arbitrary imaging geometries and X-ray propagation models;
  2. computationally efficient inversion algorithms for such models based on microlocal analysis;
  3. computationally efficient simultaneous image reconstruction and segmentation algorithms;
  4. computationally efficient filtered-backprojection algorithms that can take into account non-stationary noise;
  5. computationally efficient filtered-backprojection type image reconstruction method with sparsity constaints.

The outcomes of this research have implications, not only in X-ray CT based EDS, but also other applications involving synthetic aperture radar and sonar, as well as X-ray CT medical imaging.

At the end of this effort, we envision system level changes in explosives detection systems to accommodate generalized filtered-backprojection algorithms. We anticipate that these changes will have a direct effect on saving the lives of civilians and military personnel.
- Progress Report, Project Overview & Significance
Project Leader
  • Birsen Yazici
    Rensselaer Polytechnic Institute

Students Currently Involved in Project
  • Zhengmin Li
    Rensselaer Polytechnic Institute