10 research outputs found
A preliminary approach to intelligent x-ray imaging for baggage inspection at airports
Identifying explosives in baggage at airports relies on being able to characterize the materials that make up an X-ray image. If a suspicion is generated during the imaging process (step 1), the image data could be enhanced by adapting the scanning parameters (step 2). This paper addresses the first part of this problem and uses textural signatures to recognize and characterize materials and hence enabling system control. Directional Gabor-type filtering was applied to a series of different X-ray images. Images were processed in such a way as to simulate a line scanning geometry. Based on our experiments with images of industrial standards and our own samples it was found that different materials could be characterized in terms of the frequency range and orientation of the filters. It was also found that the signal strength generated by the filters could be used as an indicator of visibility and optimum imaging conditions predicted
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Tau pathology, microglia activation, and network dysfunction in Alzheimer’s disease
Tau pathology and neuroinflammation are key etio-pathogenetic mediators of Alzheimer’s disease (AD). Network dysfunction has also been reported in AD and linked to cognitive impairment. However, it is unclear how tau pathology and neuroinflammation contribute to network dysfunction and cognitive deficit in AD.
I study these issues by combining: 1) positron emission tomography imaging using the [18F-AV]-1451 tracer (measuring in vivo tau pathology) or [11C]PK11195 ligand (indexing in vivo neuroinflammation), with 2) connectivity measures of resting-state functional magnetic resonance imaging.
I found increased [18F-AV]-1451 binding (reflecting tau pathology) in AD patients, relative to controls, in the medial/lateral temporal and parietal cortices. In terms of functional connectivity, more strongly connected brain regions accrued more tau pathology. Increasing tau burden was also linked to progressive weakening of the connectivity across the same regions.
I also found increased [11C]PK11195 binding (reflecting neuroinflammation) in the medial/lateral temporal and parietal cortices in AD patients, relative to controls. [11C]PK11195 binding in the cuneus/precuneus correlated with episodic memory deficits in AD patients. This pattern of neuroinflammation was linked to large-scale network’ dysfunction and cognitive deficit. AD patients with enhanced neuroinflammation showed more abnormal connectivity across the whole-brain. The expression of a stronger association between altered functional connectivity and high levels of neuroinflammation related to cognitive deficit in AD.
My studies have wide-ranging implications that include:
1) the validation of animal models of tau propagation in living patients with AD;
2) improvements in our understanding of the relationship between in vivo tau pathology and brain functioning;
3) evidence for a primary role of neuroinflammation in mediating network dysfunction in AD;
4) support to the notion that immune-therapeutic strategies targeting tau pathology and neuroinflammation may be useful in AD.MRC and NIHR funde
Signal processing algorithms for enhanced image fusion performance and assessment
The dissertation presents several signal processing algorithms for image fusion in noisy multimodal
conditions. It introduces a novel image fusion method which performs well for image
sets heavily corrupted by noise. As opposed to current image fusion schemes, the method has
no requirements for a priori knowledge of the noise component. The image is decomposed with
Chebyshev polynomials (CP) being used as basis functions to perform fusion at feature level. The
properties of CP, namely fast convergence and smooth approximation, renders it ideal for heuristic
and indiscriminate denoising fusion tasks. Quantitative evaluation using objective fusion assessment
methods show favourable performance of the proposed scheme compared to previous efforts
on image fusion, notably in heavily corrupted images.
The approach is further improved by incorporating the advantages of CP with a state-of-the-art
fusion technique named independent component analysis (ICA), for joint-fusion processing
based on region saliency. Whilst CP fusion is robust under severe noise conditions, it is prone to
eliminating high frequency information of the images involved, thereby limiting image sharpness.
Fusion using ICA, on the other hand, performs well in transferring edges and other salient features
of the input images into the composite output. The combination of both methods, coupled with
several mathematical morphological operations in an algorithm fusion framework, is considered a
viable solution. Again, according to the quantitative metrics the results of our proposed approach
are very encouraging as far as joint fusion and denoising are concerned.
Another focus of this dissertation is on a novel metric for image fusion evaluation that is based
on texture. The conservation of background textural details is considered important in many fusion
applications as they help define the image depth and structure, which may prove crucial in
many surveillance and remote sensing applications. Our work aims to evaluate the performance of image fusion algorithms based on their ability to retain textural details from the fusion process.
This is done by utilising the gray-level co-occurrence matrix (GLCM) model to extract second-order
statistical features for the derivation of an image textural measure, which is then used to
replace the edge-based calculations in an objective-based fusion metric. Performance evaluation
on established fusion methods verifies that the proposed metric is viable, especially for multimodal
scenarios
Field Programmable Gate Arrays (FPGAs) II
This Edited Volume Field Programmable Gate Arrays (FPGAs) II is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of Computer and Information Science. The book comprises single chapters authored by various researchers and edited by an expert active in the Computer and Information Science research area. All chapters are complete in itself but united under a common research study topic. This publication aims at providing a thorough overview of the latest research efforts by international authors on Computer and Information Science, and open new possible research paths for further novel developments
Enhancing Real-time Embedded Image Processing Robustness on Reconfigurable Devices for Critical Applications
Nowadays, image processing is increasingly used in several application fields, such as biomedical, aerospace, or automotive. Within these fields, image processing is used to serve both non-critical and critical tasks. As example, in automotive, cameras are becoming key sensors in increasing car safety, driving assistance and driving comfort. They have been employed for infotainment (non-critical), as well as for some driver assistance tasks (critical), such as Forward Collision Avoidance, Intelligent Speed Control, or Pedestrian Detection.
The complexity of these algorithms brings a challenge in real-time image processing systems, requiring high computing capacity, usually not available in processors for embedded systems. Hardware acceleration is therefore crucial, and devices such as Field Programmable Gate Arrays (FPGAs) best fit the growing demand of computational capabilities. These devices can assist embedded processors by significantly speeding-up computationally intensive software algorithms.
Moreover, critical applications introduce strict requirements not only from the real-time constraints, but also from the device reliability and algorithm robustness points of view. Technology scaling is highlighting reliability problems related to aging phenomena, and to the increasing sensitivity of digital devices to external radiation events that can cause transient or even permanent faults. These faults can lead to wrong information processed or, in the worst case, to a dangerous system failure. In this context, the reconfigurable nature of FPGA devices can be exploited to increase the system reliability and robustness by leveraging Dynamic Partial Reconfiguration features.
The research work presented in this thesis focuses on the development of techniques for implementing efficient and robust real-time embedded image processing hardware accelerators and systems for mission-critical applications. Three main challenges have been faced and will be discussed, along with proposed solutions, throughout the thesis: (i) achieving real-time performances, (ii) enhancing algorithm robustness, and (iii) increasing overall system's dependability.
In order to ensure real-time performances, efficient FPGA-based hardware accelerators implementing selected image processing algorithms have been developed. Functionalities offered by the target technology, and algorithm's characteristics have been constantly taken into account while designing such accelerators, in order to efficiently tailor algorithm's operations to available hardware resources.
On the other hand, the key idea for increasing image processing algorithms' robustness is to introduce self-adaptivity features at algorithm level, in order to maintain constant, or improve, the quality of results for a wide range of input conditions, that are not always fully predictable at design-time (e.g., noise level variations). This has been accomplished by measuring at run-time some characteristics of the input images, and then tuning the algorithm parameters based on such estimations. Dynamic reconfiguration features of modern reconfigurable FPGA have been extensively exploited in order to integrate run-time adaptivity into the designed hardware accelerators.
Tools and methodologies have been also developed in order to increase the overall system dependability during reconfiguration processes, thus providing safe run-time adaptation mechanisms. In addition, taking into account the target technology and the environments in which the developed hardware accelerators and systems may be employed, dependability issues have been analyzed, leading to the development of a platform for quickly assessing the reliability and characterizing the behavior of hardware accelerators implemented on reconfigurable FPGAs when they are affected by such faults
Radiologie en super résolution et à double énergie
Deux méthodes sont combinées dans cet ouvrage pour automatiser le rehaussement des radiographies en super résolution et à double énergie. La première méthode, la populaire déconvolution de Lucy-Richardson, peut être optimisée s'il est possible de soustraire du signal original une information a priori tel le signal de fond. Ce signal peut être déduit par la seconde méthode qui consiste à produire une image synthétique constituée des tissus mous et de l'air présents dans la radiographie. Cette image synthétique est le fruit d'une nouvelle combinaison des images d'épaisseurs équivalentes d'aluminium et de methacrylate de polymethyle (Lucite) produites par la méthode de double énergie classique. En combinant les deux méthodes, une optimisation de la déconvolution en super résolution des images radiologiques est possible
Safety and Reliability - Safe Societies in a Changing World
The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management
- mathematical methods in reliability and safety
- risk assessment
- risk management
- system reliability
- uncertainty analysis
- digitalization and big data
- prognostics and system health management
- occupational safety
- accident and incident modeling
- maintenance modeling and applications
- simulation for safety and reliability analysis
- dynamic risk and barrier management
- organizational factors and safety culture
- human factors and human reliability
- resilience engineering
- structural reliability
- natural hazards
- security
- economic analysis in risk managemen