NeatVision: a development environment for machine vision engineers

This Chapter will detail a free image analysis development environment for machine vision engineers. The environment provides high-level access to a wide range of image manipulation, processing and analysis algorithms (over 300 to date) through a well-defined and easy to use graphical interface. Users can extend the core library using the developer’s interface, a plug-in, which features, automatic source code generation, compilation with full error feedback and dynamic algorithm updates. The Chapter will also discuss key issues associated with the environment and outline the advantages in adopting such a system for machine vision application developmen

Real-time colour recognition in symbolic programming for machine vision systems

It is impossible to collect more than a tiny proportion of all of the possible examples of a given hue to form a training set for a machine that learns to discriminate colours. In view of this, it is argued that colour generelization is essential. Three mechanisms for learning colours, as defined by a human being, are described. One of these is based upon an idea developed by A.P. Plummer and is implemented in a commercial device known as the ldquointelligent camerardquo. This implementation can learn the characteristics of coloured scenes presented to it and can segment a video image in real-time. This paper presents four procedures that allow the range of colours learned by such a system to be broadened so that recognition is made more reliable and less prone to generating noisy images that are difficult to analyse. Three of the procedures can be used to improve colour discrimination, while a fourth procedure is used when a single and general colour concept has to be learned. Several experiments were devised to demonstrate the effectiveness of colour generelization. These have shown that it is indeed possible to achieve reliable colour discrimination / recognition for such tasks as inspecting packaging and fruit. A practical system based upon the intelligent camera and controlled by software written in PROLOG has been developed by the authors and is being used in a study of methods for declarative programming of machine vision systems for industrial applications

Using A One-Class Compound Classifier To Detect In-Vehicle Network Attacks

The Controller Area Network (CAN) in vehicles provides serial communication between electronic control units that manage en- gine, transmission, steering and braking. Researchers have recently demonstrated the vulnerability of the network to cyber-attacks which can manipulate the operation of the vehicle and compromise its safety. Some proposals for CAN intrusion detection systems, that identify attacks by detecting packet anomalies, have drawn on one-class classi cation, whereby the system builds a decision surface based on a large number of normal instances. The one-class approach is discussed in this paper, together with initial results and observations from implementing a classi er new to this eld. The Compound Classier has been used in image processing and medical analysis, and holds advantages that could be relevant to CAN intrusion detection.<br/

The "zeroth law" of turbulence: Isotropic turbulence simulations revisited

The dimensionless kinetic energy dissipation rate C_epsilon is estimated from numerical simulations of statistically stationary isotropic box turbulence that is slightly compressible. The Taylor microscale Reynolds number Re_lambda range is 20 < Re_lambda < 220 and the statistical stationarity is achieved with a random phase forcing method. The strong Re_lambda dependence of C_epsilon abates when Re_lambda approx. 100 after which C_epsilon slowly approaches approx 0.5 a value slightly different to previously reported simulations but in good agreement with experimental results. If C_epsilon is estimated at a specific time step from the time series of the quantities involved it is necessary to account for the time lag between energy injection and energy dissipation. Also, the resulting value can differ from the ensemble averaged value by up to +-30%. This may explain the spread in results from previously published estimates of C_epsilon.Comment: 7 pages, 7 figures. Submitted to Phys. Rev.

Repeatability of Cerebral Perfusion Using Dynamic Susceptibility Contrast MRI in Glioblastoma Patients12

OBJECTIVES This study evaluates the repeatability of brain perfusion using dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) with a variety of post-processing methods. METHODS Thirty-two patients with newly diagnosed glioblastoma were recruited. On a 3-T MRI using a dual-echo, gradient-echo spin-echo DSC-MRI protocol, the patients were scanned twice 1 to 5 days apart. Perfusion maps including cerebral blood volume (CBV) and cerebral blood flow (CBF) were generated using two contrast agent leakage correction methods, along with testing normalization to reference tissue, and application of arterial input function (AIF). Repeatability of CBV and CBF within tumor regions and healthy tissues, identified by structural images, was assessed with intra-class correlation coefficients (ICCs) and repeatability coefficients (RCs). Coefficients of variation (CVs) were reported for selected methods. RESULTS CBV and CBF were highly repeatable within tumor with ICC values up to 0.97. However, both CBV and CBF showed lower ICCs for healthy cortical tissues (up to 0.83), healthy gray matter (up to 0.95), and healthy white matter (WM; up to 0.93). The values of CV ranged from 6% to 10% in tumor and 3% to 11% in healthy tissues. The values of RC relative to the mean value of measurement within healthy WM ranged from 22% to 42% in tumor and 7% to 43% in healthy tissues. These percentages show how much variation in perfusion parameter, relative to that in healthy WM, we expect to observe to consider it statistically significant. We also found that normalization improved repeatability, but AIF deconvolution did not. CONCLUSIONS DSC-MRI is highly repeatable in high-grade glioma patients

Early changes in glioblastoma metabolism measured by MR spectroscopic imaging during combination of anti-angiogenic cediranib and chemoradiation therapy are associated with survival

Precise assessment of treatment response in glioblastoma during combined anti-angiogenic and chemoradiation remains a challenge. In particular, early detection of treatment response by standard anatomical imaging is confounded by pseudo-response or pseudo-progression. Metabolic changes may be more specific for tumor physiology and less confounded by changes in blood-brain barrier permeability. We hypothesize that metabolic changes probed by magnetic resonance spectroscopic imaging can stratify patient response early during combination therapy. We performed a prospective longitudinal imaging study in newly diagnosed glioblastoma patients enrolled in a phase II clinical trial of the pan-vascular endothelial growth factor receptor inhibitor cediranib in combination with standard fractionated radiation and temozolomide (chemoradiation). Forty patients were imaged weekly during therapy with an imaging protocol that included magnetic resonance spectroscopic imaging, perfusion magnetic resonance imaging, and anatomical magnetic resonance imaging. Data were analyzed using receiver operator characteristics, Cox proportional hazards model, and Kaplan-Meier survival plots. We observed that the ratio of total choline to healthy creatine after 1 month of treatment was significantly associated with overall survival, and provided as single parameter: (1) the largest area under curve (0.859) in receiver operator characteristics, (2) the highest hazard ratio (HR = 85.85, P = 0.006) in Cox proportional hazards model, (3) the largest separation (P = 0.004) in Kaplan-Meier survival plots. An inverse correlation was observed between total choline/healthy creatine and cerebral blood flow, but no significant relation to tumor volumetrics was identified. Our results suggest that in vivo metabolic biomarkers obtained by magnetic resonance spectroscopic imaging may be an early indicator of response to anti-angiogenic therapy combined with standard chemoradiation in newly diagnosed glioblastoma

Valuing Health Gain from Composite Response Endpoints for Multisystem Diseases

Objectives: This study aimed to demonstrate how to estimate the value of health gain after patients with a multisystem disease achieve a condition-specific composite response endpoint. Methods: Data from patients treated in routine practice with an exemplar multisystem disease (systemic lupus erythematosus) were extracted from a national register (British Isles Lupus Assessment Group Biologics Register). Two bespoke composite response endpoints (Major Clinical Response and Improvement) were developed in advance of this study. Difference-in-differences regression compared health utility values (3-level version of EQ-5D; UK tariff) over 6 months for responders and nonresponders. Bootstrapped regression estimated the incremental quality-adjusted life-years (QALYs), probability of QALY gain after achieving the response criteria, and population monetary benefit of response. Results: Within the sample (n = 171), 18.2% achieved Major Clinical Response and 49.1% achieved Improvement at 6 months. Incremental health utility values were 0.0923 for Major Clinical Response and 0.0454 for Improvement. Expected incremental QALY gain at 6 months was 0.020 for Major Clinical Response and 0.012 for Improvement. Probability of QALY gain after achieving the response criteria was 77.6% for Major Clinical Response and 72.7% for Improvement. Population monetary benefit of response was £1 106 458 for Major Clinical Response and £649 134 for Improvement. Conclusions: Bespoke composite response endpoints are becoming more common to measure treatment response for multisystem diseases in trials and observational studies. Health technology assessment agencies face a growing challenge to establish whether these endpoints correspond with improved health gain. Health utility values can generate this evidence to enhance the usefulness of composite response endpoints for health technology assessment, decision making, and economic evaluation

The role and uses of antibodies in COVID-19 infections: a living review

Coronavirus disease 2019 has generated a rapidly evolving field of research, with the global scientific community striving for solutions to the current pandemic. Characterizing humoral responses towards SARS-CoV-2, as well as closely related strains, will help determine whether antibodies are central to infection control, and aid the design of therapeutics and vaccine candidates. This review outlines the major aspects of SARS-CoV-2-specific antibody research to date, with a focus on the various prophylactic and therapeutic uses of antibodies to alleviate disease in addition to the potential of cross-reactive therapies and the implications of long-term immunity