Abstract:
This work considers novel image-processing and computer-vision techniques to
advance the automated analysis of low-resolution, complex 3D volumetric Computed
Tomography (CT) imagery obtained in the aviation-security-screening domain.
Novel research is conducted in three key areas: image quality improvement,
segmentation and classification.
A sinogram-completion Metal Artefact Reduction (MAR) technique is presented.
The presence of multiple metal objects in the scanning Field of View
(FoV) is accounted for via a distance-driven weighting scheme. The technique is
shown to perform comparably to the state-of-the-art medical MAR techniques in
a quantitative and qualitative comparative evaluation.
A materials-based technique is proposed for the segmentation of unknown objects
from low-resolution, cluttered volumetric baggage-CT data. Initial coarse
segmentations, generated using dual-energy techniques, are refined by partitioning
at automatically-detected regions. Partitioning is guided by a novel random-forestbased
quality metric (trained to recognise high-quality, single-object segments). A
second segmentation-quality measure is presented for quantifying the quality of
full segmentations. In a comparative evaluation, the proposed method is shown to
produce similar-quality segmentations to the state-of-the-art at reduced processing
times.
A codebook model constructed using an Extremely Randomised Clustering
(ERC) forest for feature encoding, a dense-feature-sampling strategy and a Support
Vector Machine (SVM) classifier is presented. The model is shown to offer
improvements in accuracy over the state-of-the-art 3D visual-cortex model at reduced
processing times, particularly in the presence of noise and artefacts.
The overall contribution of this work is a novel, fully-automated and effcient
framework for the classification of objects in cluttered 3D baggage-CT imagery. It
extends the current state-of-the-art by improving classification performance in the
presence of noise and artefacts; by automating the previously-manual isolation of
objects and by decreasing processing times by several orders of magnitude.
