F3-D Simultaneous Segmentation and Image Reconstruction for CT X-ray Based EDS Systems Screening

Abstract:Despite recent technological advances, reliable detection of explosives in luggage and IEDs remains a challenging fundamental 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, a three dimensional x-ray attenuation images are formed, next, the content of the images are decomposed into homogenous connected regions and 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 methods based on microlocal analysis and fast back-projection algorithms. 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.

Faculty and Staff Currently Involved in Project:

Birsen Yazici
Associate Professor
Rensselaer Polytechnic Institute

Students Currently Involved in Project:

Zhengmin Li, PhD
Rensselaer Polytechnic Institute