9,873 research outputs found
Algorithms for enhanced artifact reduction and material recognition in computed tomography
Computed tomography (CT) imaging provides a non-destructive means to examine the interior of an object which is a valuable tool in medical and security applications. The variety of materials seen in the security applications is higher than in the medical applications. Factors such as clutter, presence of dense objects, and closely placed items in a bag or a parcel add to the difficulty of the material recognition in security applications. Metal and dense objects create image artifacts which degrade the image quality and deteriorate the recognition accuracy. Conventional CT machines scan the object using single source or dual source spectra and reconstruct the effective linear attenuation coefficient of voxels in the image which may not provide the sufficient information to identify the occupying materials.
In this dissertation, we provide algorithmic solutions to enhance CT material recognition. We provide a set of algorithms to accommodate different classes of CT machines. First, we provide a metal artifact reduction algorithm for conventional CT machines which perform the measurements using single X-ray source spectrum. Compared to previous methods, our algorithm is robust to severe metal artifacts and accurately reconstructs the regions that are in proximity to metal. Second, we propose a novel joint segmentation and classification algorithm for dual-energy CT machines which extends prior work to capture spatial correlation in material X-ray attenuation properties. We show that the classification performance of our method surpasses the prior work's result.
Third, we propose a new framework for reconstruction and classification using a new class of CT machines known as spectral CT which has been recently developed. Spectral CT uses multiple energy windows to scan the object, thus it captures data across higher energy dimensions per detector. Our reconstruction algorithm extracts essential features from the measured data by using spectral decomposition. We explore the effect of using different transforms in performing the measurement decomposition and we develop a new basis transform which encapsulates the sufficient information of the data and provides high classification accuracy. Furthermore, we extend our framework to perform the task of explosive detection. We show that our framework achieves high detection accuracy and it is robust to noise and variations. Lastly, we propose a combined algorithm for spectral CT, which jointly reconstructs images and labels each region in the image. We offer a tractable optimization method to solve the proposed discrete tomography problem. We show that our method outperforms the prior work in terms of both reconstruction quality and classification accuracy
Adaptive Target Recognition: A Case Study Involving Airport Baggage Screening
This work addresses the question whether it is possible to design a
computer-vision based automatic threat recognition (ATR) system so that it can
adapt to changing specifications of a threat without having to create a new ATR
each time. The changes in threat specifications, which may be warranted by
intelligence reports and world events, are typically regarding the physical
characteristics of what constitutes a threat: its material composition, its
shape, its method of concealment, etc. Here we present our design of an AATR
system (Adaptive ATR) that can adapt to changing specifications in materials
characterization (meaning density, as measured by its x-ray attenuation
coefficient), its mass, and its thickness. Our design uses a two-stage cascaded
approach, in which the first stage is characterized by a high recall rate over
the entire range of possibilities for the threat parameters that are allowed to
change. The purpose of the second stage is to then fine-tune the performance of
the overall system for the current threat specifications. The computational
effort for this fine-tuning for achieving a desired PD/PFA rate is far less
than what it would take to create a new classifier with the same overall
performance for the new set of threat specifications
Automated X-ray image analysis for cargo security: Critical review and future promise
We review the relatively immature field of automated image analysis for X-ray cargo imagery. There is increasing demand for automated analysis methods that can assist in the inspection and selection of containers, due to the ever-growing volumes of traded cargo and the increasing concerns that customs- and security-related threats are being smuggled across borders by organised crime and terrorist networks. We split the field into the classical pipeline of image preprocessing and image understanding. Preprocessing includes: image manipulation; quality improvement; Threat Image Projection (TIP); and material discrimination and segmentation. Image understanding includes: Automated Threat Detection (ATD); and Automated Contents Verification (ACV). We identify several gaps in the literature that need to be addressed and propose ideas for future research. Where the current literature is sparse we borrow from the single-view, multi-view, and CT X-ray baggage domains, which have some characteristics in common with X-ray cargo
Unsupervised Multi Class Segmentation of 3D Images with Intensity Inhomogeneities
Intensity inhomogeneities in images constitute a considerable challenge in
image segmentation. In this paper we propose a novel biconvex variational model
to tackle this task. We combine a total variation approach for multi class
segmentation with a multiplicative model to handle the inhomogeneities. Our
method assumes that the image intensity is the product of a smoothly varying
part and a component which resembles important image structures such as edges.
Therefore, we penalize in addition to the total variation of the label
assignment matrix a quadratic difference term to cope with the smoothly varying
factor. A critical point of our biconvex functional is computed by a modified
proximal alternating linearized minimization method (PALM). We show that the
assumptions for the convergence of the algorithm are fulfilled by our model.
Various numerical examples demonstrate the very good performance of our method.
Particular attention is paid to the segmentation of 3D FIB tomographical images
which was indeed the motivation of our work
Recent trends, technical concepts and components of computer-assisted orthopedic surgery systems: A comprehensive review
Computer-assisted orthopedic surgery (CAOS) systems have become one of the most important and challenging types of system in clinical orthopedics, as they enable precise treatment of musculoskeletal diseases, employing modern clinical navigation systems and surgical tools. This paper brings a comprehensive review of recent trends and possibilities of CAOS systems. There are three types of the surgical planning systems, including: systems based on the volumetric images (computer tomography (CT), magnetic resonance imaging (MRI) or ultrasound images), further systems utilize either 2D or 3D fluoroscopic images, and the last one utilizes the kinetic information about the joints and morphological information about the target bones. This complex review is focused on three fundamental aspects of CAOS systems: their essential components, types of CAOS systems, and mechanical tools used in CAOS systems. In this review, we also outline the possibilities for using ultrasound computer-assisted orthopedic surgery (UCAOS) systems as an alternative to conventionally used CAOS systems.Web of Science1923art. no. 519
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