Current aviation security relies heavily on personnel screening using X-ray backscatter systems or other advanced
imaging technologies. Passenger privacy concerns and screening times can be reduced through the use of low-dose twosided
X-ray backscatter (Bx) systems, which also have the ability to collect transmission (Tx) X-ray. Bx images reveal
objects placed on the body, such as contraband and security threats, as well as anatomical features at or close to the
surface, such as lungs cavities and bones. While the quality of the transmission images is lower than medical imagery
due to the low X-ray dose, Tx images can be of significant value in interpreting features in the Bx images, such as lung
cavities, which can cause false alarms in automated threat detection (ATD) algorithms. Here we demonstrate an ATD
processing chain fusing both Tx and BX images. The approach employs automatically extracted fiducial points on the
body and localized active contour methods to segments lungs in acquired Tx and Bx images. Additionally, we derive
metrics from the Tx image can be related to the probability of observing internal body structure in the Bx image. The
combined use of Tx and Bx data can enable improved overall system performance.
We present a complete automatic system to extract 3D centerlines of ribs from thoracic CT scans. Our rib
centerline system determines the positional information for the rib cage consisting of extracted rib centerlines,
spinal canal centerline, pairing and labeling of ribs. We show an application of this output to produce an enhanced
visualization of the rib cage by the method of Kiraly et al., in which the ribs are digitally unfolded along their
centerlines. The centerline extraction consists of three stages: (a) pre-trace processing for rib localization, (b) rib
centerline tracing, and (c) post-trace processing to merge the rib traces. Then we classify ribs from non-ribs and
determine anatomical rib labeling. Our novel centerline tracing technique uses the Random Walker algorithm
to segment the structural boundary of the rib in successive 2D cross sections orthogonal to the longitudinal
direction of the ribs. Then the rib centerline is progressively traced along the rib using a 3D Kalman filter.
The rib centerline extraction framework was evaluated on 149 CT datasets with varying slice spacing, dose, and
under a variety of reconstruction kernels. The results of the evaluation are presented. The extraction takes
approximately 20 seconds on a modern radiology workstation and performs robustly even in the presence of
partial volume effects or rib pathologies such as bone metastases or fractures, making the system suitable for
assisting clinicians in expediting routine rib reading for oncology and trauma applications.
Image segmentation using the piecewise smooth variational model proposed by Mumford and Shah is both robust and computationally
expensive. Fortunately, both the intermediate segmentations computed in the process of the evolution, and the
final segmentation itself have a common structure. They typically resemble a linear combination of blurred versions of the
original image. In this paper, we present methods for fast approximations to Mumford-Shah segmentation using reduced
image bases. We show that the majority of the robustness of Mumford-Shah segmentation can be obtained without allowing
each pixel to vary independently in the implementation. We illustrate segmentations of real images that show how the
proposed segmentation method is both computationally inexpensive, and has comparable performance to Mumford-Shah
segmentations where each pixel is allowed to vary freely.
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