Brain Tumour Classification Using Deep Features from Structural MRI

C

Optical Methods in Sensing and Imaging for Medical and Biological Applications

The recent advances in optical sources and detectors have opened up new opportunities for sensing and imaging techniques which can be successfully used in biomedical and healthcare applications. This book, entitled ‘Optical Methods in Sensing and Imaging for Medical and Biological Applications’, focuses on various aspects of the research and development related to these areas. The book will be a valuable source of information presenting the recent advances in optical methods and novel techniques, as well as their applications in the fields of biomedicine and healthcare, to anyone interested in this subject

Optical flow estimation via steered-L1 norm

Global variational methods for estimating optical flow are among the best performing methods due to the subpixel accuracy and the ‘fill-in’ effect they provide. The fill-in effect allows optical flow displacements to be estimated even in low and untextured areas of the image. The estimation of such displacements are induced by the smoothness term. The L1 norm provides a robust regularisation term for the optical flow energy function with a very good performance for edge-preserving. However this norm suffers from several issues, among these is the isotropic nature of this norm which reduces the fill-in effect and eventually the accuracy of estimation in areas near motion boundaries. In this paper we propose an enhancement to the L1 norm that improves the fill-in effect for this smoothness term. In order to do this we analyse the structure tensor matrix and use its eigenvectors to steer the smoothness term into components that are ‘orthogonal to’ and ‘aligned with’ image structures. This is done in primal-dual formulation. Results show a reduced end-point error and improved accuracy compared to the conventional L1 norm

Optical flow estimation via steered-L1 norm

Global variational methods for estimating optical flow are among the best performing methods due to the subpixel accuracy and the ‘fill-in’ effect they provide. The fill-in effect allows optical flow displacements to be estimated even in low and untextured areas of the image. The estimation of such displacements are induced by the smoothness term. The L1 norm provides a robust regularisation term for the optical flow energy function with a very good performance for edge-preserving. However this norm suffers from several issues, among these is the isotropic nature of this norm which reduces the fill-in effect and eventually the accuracy of estimation in areas near motion boundaries. In this paper we propose an enhancement to the L1 norm that improves the fill-in effect for this smoothness term. In order to do this we analyse the structure tensor matrix and use its eigenvectors to steer the smoothness term into components that are ‘orthogonal to’ and ‘aligned with’ image structures. This is done in primal-dual formulation. Results show a reduced end-point error and improved accuracy compared to the conventional L1 norm

Enabling the Development and Implementation of Digital Twins : Proceedings of the 20th International Conference on Construction Applications of Virtual Reality

Welcome to the 20th International Conference on Construction Applications of Virtual Reality (CONVR 2020). This year we are meeting on-line due to the current Coronavirus pandemic. The overarching theme for CONVR2020 is "Enabling the development and implementation of Digital Twins". CONVR is one of the world-leading conferences in the areas of virtual reality, augmented reality and building information modelling. Each year, more than 100 participants from all around the globe meet to discuss and exchange the latest developments and applications of virtual technologies in the architectural, engineering, construction and operation industry (AECO). The conference is also known for having a unique blend of participants from both academia and industry. This year, with all the difficulties of replicating a real face to face meetings, we are carefully planning the conference to ensure that all participants have a perfect experience. We have a group of leading keynote speakers from industry and academia who are covering up to date hot topics and are enthusiastic and keen to share their knowledge with you. CONVR participants are very loyal to the conference and have attended most of the editions over the last eighteen editions. This year we are welcoming numerous first timers and we aim to help them make the most of the conference by introducing them to other participants

Recent Advances in Volatile Organic Compound Analysis as Diagnostic Biomarkers

Volatile organic compounds (VOCs) are a diverse group of carbon-based molecules that are volatile at ambient temperatures and are emitted by an organism as a result of metabolic processes of cells and associated microbiome. The qualitative and quantitative profile of VOCs in biological fluids can vary depending on the physiological changes. Therefore, the pattern of volatile metabolites may reflect the presence of several diseases. This has been intensively investigated in the last few decades, resulting in an increasing number of studies focused on new volatile biomarker discovery.This reprint aimed to summarize the recent findings related to VOCs detected in various biological fluids such as breath, urine and feces for biomedical applications. The content covers various topics, including but not limited to biomedical/medical application of VOC analysis, biomarker discovery, and novel approaches for sampling and analyzing VOCs