Visualization And Processing Of Higher Order Descriptors For Multi Valued Data
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Visualization And Processing Of Higher Order Descriptors For Multi Valued Data
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Author : Ingrid Hotz
language : en
Publisher: Springer
Release Date : 2015-07-03
Visualization And Processing Of Higher Order Descriptors For Multi Valued Data written by Ingrid Hotz and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-07-03 with Mathematics categories.
Modern imaging techniques and computational simulations yield complex multi-valued data that require higher-order mathematical descriptors. This book addresses topics of importance when dealing with such data, including frameworks for image processing, visualization and statistical analysis of higher-order descriptors. It also provides examples of the successful use of higher-order descriptors in specific applications and a glimpse of the next generation of diffusion MRI. To do so, it combines contributions on new developments, current challenges in this area and state-of-the-art surveys. Compared to the increasing importance of higher-order descriptors in a range of applications, tools for analysis and processing are still relatively hard to come by. Even though application areas such as medical imaging, fluid dynamics and structural mechanics are very different in nature they face many shared challenges. This book provides an interdisciplinary perspective on this topic with contributions from key researchers in disciplines ranging from visualization and image processing to applications. It is based on the 5th Dagstuhl seminar on Visualization and Processing of Higher Order Descriptors for Multi-Valued Data. This book will appeal to scientists who are working to develop new analysis methods in the areas of image processing and visualization, as well as those who work with applications that generate higher-order data or could benefit from higher-order models and are searching for novel analytical tools.
Visualization And Processing Of Tensors And Higher Order Descriptors For Multi Valued Data
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Author : Carl-Fredrik Westin
language : en
Publisher: Springer
Release Date : 2014-07-17
Visualization And Processing Of Tensors And Higher Order Descriptors For Multi Valued Data written by Carl-Fredrik Westin and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-07-17 with Mathematics categories.
Arising from the fourth Dagstuhl conference entitled Visualization and Processing of Tensors and Higher Order Descriptors for Multi-Valued Data (2011), this book offers a broad and vivid view of current work in this emerging field. Topics covered range from applications of the analysis of tensor fields to research on their mathematical and analytical properties. Part I, Tensor Data Visualization, surveys techniques for visualization of tensors and tensor fields in engineering, discusses the current state of the art and challenges, and examines tensor invariants and glyph design, including an overview of common glyphs. The second Part, Representation and Processing of Higher-order Descriptors, describes a matrix representation of local phase, outlines mathematical morphological operations techniques, extended for use in vector images, and generalizes erosion to the space of diffusion weighted MRI. Part III, Higher Order Tensors and Riemannian-Finsler Geometry, offers powerful mathematical language to model and analyze large and complex diffusion data such as High Angular Resolution Diffusion Imaging (HARDI) and Diffusion Kurtosis Imaging (DKI). A Part entitled Tensor Signal Processing presents new methods for processing tensor-valued data, including a novel perspective on performing voxel-wise morphometry of diffusion tensor data using kernel-based approach, explores the free-water diffusion model, and reviews proposed approaches for computing fabric tensors, emphasizing trabecular bone research. The last Part, Applications of Tensor Processing, discusses metric and curvature tensors, two of the most studied tensors in geometry processing. Also covered is a technique for diagnostic prediction of first-episode schizophrenia patients based on brain diffusion MRI data. The last chapter presents an interactive system integrating the visual analysis of diffusion MRI tractography with data from electroencephalography.
Medical Image Computing And Computer Assisted Intervention Miccai 2016
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Author : Sebastien Ourselin
language : en
Publisher: Springer
Release Date : 2016-10-17
Medical Image Computing And Computer Assisted Intervention Miccai 2016 written by Sebastien Ourselin and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-17 with Computers categories.
The three-volume set LNCS 9900, 9901, and 9902 constitutes the refereed proceedings of the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, held in Athens, Greece, in October 2016. Based on rigorous peer reviews, the program committee carefully selected 228 revised regular papers from 756 submissions for presentation in three volumes. The papers have been organized in the following topical sections: Part I: brain analysis, brain analysis - connectivity; brain analysis - cortical morphology; Alzheimer disease; surgical guidance and tracking; computer aided interventions; ultrasound image analysis; cancer image analysis; Part II: machine learning and feature selection; deep learning in medical imaging; applications of machine learning; segmentation; cell image analysis; Part III: registration and deformation estimation; shape modeling; cardiac and vascular image analysis; image reconstruction; and MR image analysis.
Applications Of Evolutionary Computation
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Author : Giovanni Squillero
language : en
Publisher: Springer
Release Date : 2017-04-03
Applications Of Evolutionary Computation written by Giovanni Squillero and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-04-03 with Computers categories.
The two volumes LNCS 10199 and 10200 constitute the refereed conference proceedings of the 20th European Conference on the Applications of Evolutionary Computation, EvoApplications 2017, held in Amsterdam, The Netherlands, in April 2017, collocated with the Evo* 2016 events EuroGP, EvoCOP, and EvoMUSART. The 46 revised full papers presented together with 26 poster papers were carefully reviewed and selected from 108 submissions. EvoApplications 2016 consisted of the following 13 tracks: EvoBAFIN (natural computing methods in business analytics and finance), EvoBIO (evolutionary computation, machine learning and data mining in computational biology), EvoCOMNET (nature-inspired techniques for telecommunication networks and other parallel and distributed systems), EvoCOMPLEX (evolutionary algorithms and complex systems), EvoENERGY (evolutionary computation in energy applications), EvoGAMES (bio-inspired algorithms in games), EvoIASP (evolutionary computation in image analysis, signal processing, and pattern recognition), EvoINDUSTRY (nature-inspired techniques in industrial settings), EvoKNOW (knowledge incorporation in evolutionary computation), EvoNUM (bio-inspired algorithms for continuous parameter optimization), EvoPAR (parallel implementation of evolutionary algorithms), EvoROBOT (evolutionary robotics), EvoSET (nature-inspired algorithms in software engineering and testing), and EvoSTOC (evolutionary algorithms in stochastic and dynamic environments).
Anisotropy Across Fields And Scales
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Author : Evren Özarslan
language : en
Publisher: Springer Nature
Release Date : 2021-02-10
Anisotropy Across Fields And Scales written by Evren Özarslan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-10 with Mathematics categories.
This open access book focuses on processing, modeling, and visualization of anisotropy information, which are often addressed by employing sophisticated mathematical constructs such as tensors and other higher-order descriptors. It also discusses adaptations of such constructs to problems encountered in seemingly dissimilar areas of medical imaging, physical sciences, and engineering. Featuring original research contributions as well as insightful reviews for scientists interested in handling anisotropy information, it covers topics such as pertinent geometric and algebraic properties of tensors and tensor fields, challenges faced in processing and visualizing different types of data, statistical techniques for data processing, and specific applications like mapping white-matter fiber tracts in the brain. The book helps readers grasp the current challenges in the field and provides information on the techniques devised to address them. Further, it facilitates the transfer of knowledge between different disciplines in order to advance the research frontiers in these areas. This multidisciplinary book presents, in part, the outcomes of the seventh in a series of Dagstuhl seminars devoted to visualization and processing of tensor fields and higher-order descriptors, which was held in Dagstuhl, Germany, on October 28–November 2, 2018.
See Through
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Author : Jochen Jankowai
language : en
Publisher: Linköping University Electronic Press
Release Date : 2024-12-13
See Through written by Jochen Jankowai and has been published by Linköping University Electronic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-13 with categories.
The problem of visualising multivariate data and tensor fields inherits its complexity from the data it targets. By definition, complex data is "hard to separate, analyse, or solve"1. This becomes evident through the fact that methods for "simple" data such as scalars and vectors do not trivially extend to multivariate data and tensors. In the light of increasing number of output variables from simulation models and measurements, this lack of methods leads to a limited choice in the analysis and to a lower fidelity of the analysis. In addition, split application of established methods to a subset of the data, for example the separate rendering of isosurfaces for the different scalar fields contained in multivariate data, brings about a number of challenges and pitfalls. In this work I present several approaches to extending existing methods for scalar field visualisation and analysis to multivariate data and, in some cases by extension, tensor fields. Specifically, I have investigated the extraction of isosurfaces from multivariate data, the topological analysis of multivariate data and tensor fields, and the design of transfer functions for tensor fields. Isosurfaces (contours) are a widely used visualisation modality. They can be used to intuitively highlight regions of interest and are the goto choice for taking snapshots during large-scale in-situ simulations to verify results. In domains such as meteorology where simulations yield a number of output variables for pressure, temperature, precipitation, etc., methods for visualising multivariate isosurfaces are needed. Feature level sets offer such a method by interpreting an isosurface as the result of an intersection of the isovalue with the data in the domain. From this, we expand the notion of isovalues, in this context called traits, and isosurfaces to arbitrary dimensionality. An intermediate product of the calculation of feature level sets is the distance field defining every data point’s distance towards the trait. Given this distance field, we compute the merge tree for it and thereby enable topological analysis of multivariate data. The choice of merge trees comes naturally as minima in the distance field correspond to regions closest to the trait. The concept of derived fields as input is also used in our approach to topological analysis of tensor fields. Special attention needs to be paid to the non-linear behaviour of derived vector and scalar fields. We use the field of eigenvectors derived from the tensor field to determine cells containing degenerate points in tensor fields and insert zero-valued points in the corresponding anisotropy field. This process yields a scalar field which can subsequently be used as input for further topological analysis.Another challenge when it comes to the visualisation of tensor fields is the design of transfer functions in the context of volume rendering. This is because of the high dimensional entity that is a tensor and its non-linear derivatives. We span a shape space which is populated by representatives which visually encode the tensor. This allows the user to steer the rendering by selecting the desired "shape" of the tensor rather than adjusting a slider for a derived scalar value. 1 Merriam-Webster. Complex. In Merriam-Webster dictionary (Merriam-Webster.com). Retrieved December 1st, 2024, from https://www.merriam-webster.com/dictionary/complex Problemet med att visualisera multivariat data och tensorfält beror på komplexiteten hos själva datan. Enligt definitionen består komplexa data av "många delar som hänger samman på ett svåröverskådligt sätt"2. Detta blir uppenbart genom det faktum att metoder för 'enkla' data, såsom skalärer och vektorer, inte på ett trivialt sätt går att utvidga till multivariat data och tensorer. På grund av det ökande antalet outputvariabler från simuleringsmodeller och mätningar leder denna brist till ett begränsat val av metoder i analysen och till en lägre analystrohet. Dessutom medför en uppdelad tillämpning av etablerade metoder på en delmängd av data, till exempel separat rendering av isoytor för de olika skalära fälten som ingår i multivariat data, ett antal utmaningar och fallgropar. I detta arbete presenterar jag flera tillvägagångssätt för att utvidga befintliga metoder för skalärfältsvisualisering och analys till multivariat data och, i vissa fall, i förlängningen, tensorfält. Specifikt har jag undersökt extraktion av isoytor från multivariat data, den topologiska analysen av multivariat data och tensorfält samt designen av överföringsfunktioner för tensorfält. Isoytor (konturer) är en välkänd visualiseringsteknik. De kan användas för att intuitivt lyfta fram områden av intresse och är det naturliga valet för att ta ögonblicksbilder under storskaliga simuleringar på plats för att verifiera resultat. Inom områden som meteorologi där simuleringar ger ett antal utdatavariabler för tryck, temperatur, nederbörd etc. behövs metoder för att visualisera multivariata isoytor. Feature level sets erbjuder en sådan metod genom att tolka en isoyta som resultatet av en skärning av isovärdet med data i domänen. Genom detta utökar vi begreppet isovärden, i detta sammanhang kallade traits, och isoytor till godtycklig dimensionalitet. En mellanprodukt av beräkningen av feature level sets är avståndsfältet som definierar varje datapunkts avstånd till trait:en. Med tanke på detta avståndsfält beräknar vi merge trees för det och möjliggör därigenom topologisk analys av multivariata data. Valet av merge trees kommer naturligt då minima i avståndsfältet motsvarar regioner närmast trait:en. Konceptet med beräknade fält som input används också i vårt förhållningssätt till topologisk analys av tensorfält. Det icke-linjära beteendet hos härledda/uträknade vektor- och skalära fält bör här ägnas särskild uppmärksamhet. Vi använder fältet av egenvektorer som härleds från tensorfältet för att bestämma celler som innehåller degenererade punkter i tensorfält och infogar nollvärdespunkter i motsvarande anisotropifält. Denna process ger ett skalärt fält som sedan kan användas som input för ytterligare topologisk analys. En annan utmaning när det kommer till visualisering av tensorfält är utformningen av överföringsfunktioner i samband med volymrendering. Detta beror på den högdimensionella enheten som är en tensor och dess icke-linjära derivator. Vi erbjuder ett bredd designutrymme för att visuellt koda tensorn. Detta gör att användaren kan styra renderingen genom att välja önskad "form" av tensorn istället för att justera en skjutreglage för ett härlett skalärt värde. 2 Svensk ordbok. Komplex. I Svenska Akademiens ordbok (svenska.se). Hämtad den 1:a december 2024 från https://svenska.se/so/?id=140703_ 1&pz=3
Handbook Of Neuroimaging Data Analysis
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Author : Hernando Ombao
language : en
Publisher: CRC Press
Release Date : 2016-11-18
Handbook Of Neuroimaging Data Analysis written by Hernando Ombao and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-18 with Mathematics categories.
This book explores various state-of-the-art aspects behind the statistical analysis of neuroimaging data. It examines the development of novel statistical approaches to model brain data. Designed for researchers in statistics, biostatistics, computer science, cognitive science, computer engineering, biomedical engineering, applied mathematics, physics, and radiology, the book can also be used as a textbook for graduate-level courses in statistics and biostatistics or as a self-study reference for Ph.D. students in statistics, biostatistics, psychology, neuroscience, and computer science.
Representation Surfaces For Physical Properties Of Materials
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Author : Manuel Laso
language : en
Publisher: Springer Nature
Release Date : 2020-04-04
Representation Surfaces For Physical Properties Of Materials written by Manuel Laso and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-04 with Technology & Engineering categories.
This textbook presents all the mathematical and physical concepts needed to visualize and understand representation surfaces, providing readers with a reliable and intuitive understanding of the behavior and properties of anisotropic materials, and a sound grasp of the directionality of material properties. They will learn how to extract quantitative information from representation surfaces, which encode tremendous amounts of information in a very concise way, making them especially useful in understanding higher order tensorial material properties (piezoelectric moduli, elastic compliance and rigidity, etc.) and in the design of applications based on these materials. Readers will also learn from scratch concepts on crystallography, symmetry and Cartesian tensors, which are essential for understanding anisotropic materials, their design and application. The book describes how to apply representation surfaces to a diverse range of material properties, making it a valuable resource for material scientists, mechanical engineers, and solid state physicists, as well as advanced undergraduates in Materials Science, Solid State Physics, Electronics, Optics, Mechanical Engineering, Composites and Polymer Science. Moreover, the book includes a wealth of worked-out examples, problems and exercises to help further understanding.
Medical Image Computing And Computer Assisted Intervention Miccai 2021
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Author : Marleen de Bruijne
language : en
Publisher: Springer Nature
Release Date : 2021-09-23
Medical Image Computing And Computer Assisted Intervention Miccai 2021 written by Marleen de Bruijne and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-23 with Computers categories.
The eight-volume set LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907, and 12908 constitutes the refereed proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, held in Strasbourg, France, in September/October 2021.* The 531 revised full papers presented were carefully reviewed and selected from 1630 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: image segmentation Part II: machine learning - self-supervised learning; machine learning - semi-supervised learning; and machine learning - weakly supervised learning Part III: machine learning - advances in machine learning theory; machine learning - attention models; machine learning - domain adaptation; machine learning - federated learning; machine learning - interpretability / explainability; and machine learning - uncertainty Part IV: image registration; image-guided interventions and surgery; surgical data science; surgical planning and simulation; surgical skill and work flow analysis; and surgical visualization and mixed, augmented and virtual reality Part V: computer aided diagnosis; integration of imaging with non-imaging biomarkers; and outcome/disease prediction Part VI: image reconstruction; clinical applications - cardiac; and clinical applications - vascular Part VII: clinical applications - abdomen; clinical applications - breast; clinical applications - dermatology; clinical applications - fetal imaging; clinical applications - lung; clinical applications - neuroimaging - brain development; clinical applications - neuroimaging - DWI and tractography; clinical applications - neuroimaging - functional brain networks; clinical applications - neuroimaging – others; and clinical applications - oncology Part VIII: clinical applications - ophthalmology; computational (integrative) pathology; modalities - microscopy; modalities - histopathology; and modalities - ultrasound *The conference was held virtually.
Computational Diffusion Mri
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Author : Andrea Fuster
language : en
Publisher: Springer
Release Date : 2016-04-08
Computational Diffusion Mri written by Andrea Fuster and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-04-08 with Mathematics categories.
These Proceedings of the 2015 MICCAI Workshop “Computational Diffusion MRI” offer a snapshot of the current state of the art on a broad range of topics within the highly active and growing field of diffusion MRI. The topics vary from fundamental theoretical work on mathematical modeling, to the development and evaluation of robust algorithms, new computational methods applied to diffusion magnetic resonance imaging data, and applications in neuroscientific studies and clinical practice. Over the last decade interest in diffusion MRI has exploded. The technique provides unique insights into the microstructure of living tissue and enables in-vivo connectivity mapping of the brain. Computational techniques are key to the continued success and development of diffusion MRI and to its widespread transfer into clinical practice. New processing methods are essential for addressing issues at each stage of the diffusion MRI pipeline: acquisition, reconstruction, modeling and model fitting, image processing, fiber tracking, connectivity mapping, visualization, group studies and inference. This volume, which includes both careful mathematical derivations and a wealth of rich, full-color visualizations and biologically or clinically relevant results, offers a valuable starting point for anyone interested in learning about computational diffusion MRI and mathematical methods for mapping brain connectivity, as well as new perspectives and insights on current research challenges for those currently working in the field. It will be of interest to researchers and practitioners in the fields of computer science, MR physics, and applied mathematics.