Multi-texture image segmentation

Visual perception of images is closely related to the recognition of the different texture areas within an image. Identifying the boundaries of these regions is an important step in image analysis and image understanding. This thesis presents supervised and unsupervised methods which allow an efficient segmentation of the texture regions within multi-texture images. The features used by the methods are based on a measure of the fractal dimension of surfaces in several directions, which allows the transformation of the image into a set of feature images, however no direct measurement of the fractal dimension is made. Using this set of features, supervised and unsupervised, statistical processing schemes are presented which produce low classification error rates. Natural texture images are examined with particular application to the analysis of sonar images of the seabed. A number of processes based on fractal models for texture synthesis are also presented. These are used to produce realistic images of natural textures, again with particular reference to sonar images of the seabed, and which show the importance of phase and directionality in our perception of texture. A further extension is shown to give possible uses for image coding and object identification

Large-scale phenotyping of patients with long COVID post-hospitalization reveals mechanistic subtypes of disease

One in ten severe acute respiratory syndrome coronavirus 2 infections result in prolonged symptoms termed long coronavirus disease (COVID), yet disease phenotypes and mechanisms are poorly understood1. Here we profiled 368 plasma proteins in 657 participants ≥3 months following hospitalization. Of these, 426 had at least one long COVID symptom and 233 had fully recovered. Elevated markers of myeloid inflammation and complement activation were associated with long COVID. IL-1R2, MATN2 and COLEC12 were associated with cardiorespiratory symptoms, fatigue and anxiety/depression; MATN2, CSF3 and C1QA were elevated in gastrointestinal symptoms and C1QA was elevated in cognitive impairment. Additional markers of alterations in nerve tissue repair (SPON-1 and NFASC) were elevated in those with cognitive impairment and SCG3, suggestive of brain–gut axis disturbance, was elevated in gastrointestinal symptoms. Severe acute respiratory syndrome coronavirus 2-specific immunoglobulin G (IgG) was persistently elevated in some individuals with long COVID, but virus was not detected in sputum. Analysis of inflammatory markers in nasal fluids showed no association with symptoms. Our study aimed to understand inflammatory processes that underlie long COVID and was not designed for biomarker discovery. Our findings suggest that specific inflammatory pathways related to tissue damage are implicated in subtypes of long COVID, which might be targeted in future therapeutic trials

SARS-CoV-2-specific nasal IgA wanes 9 months after hospitalisation with COVID-19 and is not induced by subsequent vaccination

BACKGROUND: Most studies of immunity to SARS-CoV-2 focus on circulating antibody, giving limited insights into mucosal defences that prevent viral replication and onward transmission. We studied nasal and plasma antibody responses one year after hospitalisation for COVID-19, including a period when SARS-CoV-2 vaccination was introduced. METHODS: In this follow up study, plasma and nasosorption samples were prospectively collected from 446 adults hospitalised for COVID-19 between February 2020 and March 2021 via the ISARIC4C and PHOSP-COVID consortia. IgA and IgG responses to NP and S of ancestral SARS-CoV-2, Delta and Omicron (BA.1) variants were measured by electrochemiluminescence and compared with plasma neutralisation data. FINDINGS: Strong and consistent nasal anti-NP and anti-S IgA responses were demonstrated, which remained elevated for nine months (p < 0.0001). Nasal and plasma anti-S IgG remained elevated for at least 12 months (p < 0.0001) with plasma neutralising titres that were raised against all variants compared to controls (p < 0.0001). Of 323 with complete data, 307 were vaccinated between 6 and 12 months; coinciding with rises in nasal and plasma IgA and IgG anti-S titres for all SARS-CoV-2 variants, although the change in nasal IgA was minimal (1.46-fold change after 10 months, p = 0.011) and the median remained below the positive threshold determined by pre-pandemic controls. Samples 12 months after admission showed no association between nasal IgA and plasma IgG anti-S responses (R = 0.05, p = 0.18), indicating that nasal IgA responses are distinct from those in plasma and minimally boosted by vaccination. INTERPRETATION: The decline in nasal IgA responses 9 months after infection and minimal impact of subsequent vaccination may explain the lack of long-lasting nasal defence against reinfection and the limited effects of vaccination on transmission. These findings highlight the need to develop vaccines that enhance nasal immunity. FUNDING: This study has been supported by ISARIC4C and PHOSP-COVID consortia. ISARIC4C is supported by grants from the National Institute for Health and Care Research and the Medical Research Council. Liverpool Experimental Cancer Medicine Centre provided infrastructure support for this research. The PHOSP-COVD study is jointly funded by UK Research and Innovation and National Institute of Health and Care Research. The funders were not involved in the study design, interpretation of data or the writing of this manuscript

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Bayesian Inference and Sidescan Restoration

We consider a Bayesian approach to the problem of inferring parameters of the sonar environment given only the gathered sidescan image. A simplified model of the process is developed along with suitable prior distributions on the parameters, and a sampling technique is utilised to estimate the parameters most likely given the data. As an example, we apply this technique to estimation of a step-gain TVG curve and use the results in restoration of legacy sidescan data

Spatial Stochastic Models for Seabed Object Detection

We introduce two statistical models designed to detect discrete objects in sidescan SONAR which consider complimetary approaches to the problem. The first considers a complex textural model for the objects and implements detection through a dual hypothesis on texture class presence, while the second implements a complex Gibbs field model of the image and utilises prior knowledge of typical object morphologies to support its detection rate. The models are demonstrated on examples of different seabed sediments and object types, and are shown to be reliable in operation. The common theme of the two models is use of spatial context in analysis, which, we argue, is a very powerful technique for improving the flexibility and reliability of any analysis system. Keywords: Spatial Models, Multidimensional Log-normal distribution, Bayesian Image Reconstruction, Gibbs Fields, Markov Chain Monte Carlo techniques 1. INTRODUCTION We consider in this paper the problem of detection of discrete obje..

Seabed Classification Through Multifractal Analysis of Sidescan Sonar Imagery

This paper presents a technique for the classification and analysis of seabed sediments from sidescan sonar image, the origins of which lie in the body of fractal theory. Six seabed types were analysed, namely clay, mud, sand, gravel, stones and rock. These data sets have previously been analysed by several authors who have used techniques based on the power spectrum. The method proposed in this paper allows frequency information to be obtained but without the use of large windows which are generally required for frequency domain measurements. Results are presented for the classification of individual ground-truthed sediments and the segmentation of composite images containing these sediments. Correct classification rates of greater then 99% have been obtained and good segmentation accuracy achieved. Keywords:- swathe seabed classification, sidescan sonar, fractals. Defence Research Agency, Newton&apos;s Road, Weymoth, Dorset y Department of Computing and Electrical Engineering, Heriot..