Riemannian Geometric Statistics in Medical Image Analysis

Over the past 15 years, there has been a growing need in the medical image computing community for principled methods to process nonlinear geometric data. Riemannian geometry has emerged as one of the most powerful mathematical and computational frameworks for analyzing such data. Riemannian Geometric Statistics in Medical Image Analysis is a complete reference on statistics on Riemannian manifolds and more general nonlinear spaces with applications in medical image analysis. It provides an introduction to the core methodology followed by a presentation of state-of-the-art methods. Content includes: - The foundations of Riemannian geometric methods for statistics on manifolds with emphasis on concepts rather than on proofs - Applications of statistics on manifolds and shape spaces in medical image computing - Diffeomorphic deformations and their applications As the methods described apply to domains such as signal processing (radar signal processing and brain computer interaction), computer vision (object and face recognition), and other domains where statistics of geometric features appear, this book is suitable for researchers and graduate students in medical imaging, engineering and computer science. - A complete reference covering both the foundations and state-of-the-art methods - Edited and authored by leading researchers in the field - Contains theory, examples, applications, and algorithms - Gives an overview of current research challenges and future applications

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  • Author : Xavier Pennec
  • Publisher : Academic Press
  • Pages : 636 pages
  • ISBN : 0128147253
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKRiemannian Geometric Statistics in Medical Image Analysis

Riemannian Geometric Statistics in Medical Image Analysis

Riemannian Geometric Statistics in Medical Image Analysis
  • Author : Xavier Pennec,Stefan Sommer,Tom Fletcher
  • Publisher : Academic Press
  • Release : 01 September 2019
GET THIS BOOKRiemannian Geometric Statistics in Medical Image Analysis

Over the past 15 years, there has been a growing need in the medical image computing community for principled methods to process nonlinear geometric data. Riemannian geometry has emerged as one of the most powerful mathematical and computational frameworks for analyzing such data. Riemannian Geometric Statistics in Medical Image Analysis is a complete reference on statistics on Riemannian manifolds and more general nonlinear spaces with applications in medical image analysis. It provides an introduction to the core methodology followed by a

Riemannian Geometric Statistics in Medical Image Analysis

Riemannian Geometric Statistics in Medical Image Analysis
  • Author : Xavier Pennec,Stefan Sommer,Tom Fletcher
  • Publisher : Academic Press
  • Release : 02 September 2019
GET THIS BOOKRiemannian Geometric Statistics in Medical Image Analysis

Over the past 15 years, there has been a growing need in the medical image computing community for principled methods to process nonlinear geometric data. Riemannian geometry has emerged as one of the most powerful mathematical and computational frameworks for analyzing such data. Riemannian Geometric Statistics in Medical Image Analysis is a complete reference on statistics on Riemannian manifolds and more general nonlinear spaces with applications in medical image analysis. It provides an introduction to the core methodology followed by a

Shape in Medical Imaging

Shape in Medical Imaging
  • Author : Martin Reuter,Christian Wachinger,Hervé Lombaert,Beatriz Paniagua,Orcun Goksel,Islem Rekik
  • Publisher : Springer Nature
  • Release : 02 October 2020
GET THIS BOOKShape in Medical Imaging

This book constitutes the proceedings of the International Workshop on Shape in Medical Imaging, ShapeMI 2020, which was held in conjunction with the 23rd International Conference on Medical Image Computing and Computer Assistend Intervention, MICCAI 2020, in October 2020. The conference was planned to take place in Lima, Peru, but changed to a virtual format due to the COVID-19 pandemic. The 12 full papers included in this volume were carefully reviewed and selected from 18 submissions. They were organized in topical sections named: methods; learning;

Digital Anatomy

Digital Anatomy
  • Author : Jean-François Uhl,Joaquim Jorge,Daniel Simões Lopes,Pedro F. Campos
  • Publisher : Springer Nature
  • Release : 14 May 2021
GET THIS BOOKDigital Anatomy

This book offers readers fresh insights on applying Extended Reality to Digital Anatomy, a novel emerging discipline. Indeed, the way professors teach anatomy in classrooms is changing rapidly as novel technology-based approaches become ever more accessible. Recent studies show that Virtual (VR), Augmented (AR), and Mixed-Reality (MR) can improve both retention and learning outcomes. Readers will find relevant tutorials about three-dimensional reconstruction techniques to perform virtual dissections. Several chapters serve as practical manuals for students and trainers in anatomy to

Object Oriented Data Analysis

Object Oriented Data Analysis
  • Author : J. S. Marron,Ian L. Dryden
  • Publisher : CRC Press
  • Release : 18 November 2021
GET THIS BOOKObject Oriented Data Analysis

Object Oriented Data Analysis is a framework that facilitates inter-disciplinary research through new terminology for discussing the often many possible approaches to the analysis of complex data. Such data are naturally arising in a wide variety of areas. This book aims to provide ways of thinking that enable the making of sensible choices. The main points are illustrated with many real data examples, based on the authors' personal experiences, which have motivated the invention of a wide array of analytic

Medical Image Computing and Computer Assisted Intervention – MICCAI 2021

Medical Image Computing and Computer Assisted Intervention – MICCAI 2021
  • Author : Marleen de Bruijne,Philippe C. Cattin,Stéphane Cotin,Nicolas Padoy,Stefanie Speidel,Yefeng Zheng,Caroline Essert
  • Publisher : Springer Nature
  • Release : 22 September 2021
GET THIS BOOKMedical Image Computing and Computer Assisted Intervention – MICCAI 2021

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:

Processing, Analyzing and Learning of Images, Shapes, and Forms:

Processing, Analyzing and Learning of Images, Shapes, and Forms:
  • Author : Xue-Cheng Tai
  • Publisher : North Holland
  • Release : 01 October 2019
GET THIS BOOKProcessing, Analyzing and Learning of Images, Shapes, and Forms:

Processing, Analyzing and Learning of Images, Shapes, and Forms: Part 2, Volume 20, surveys the contemporary developments relating to the analysis and learning of images, shapes and forms, covering mathematical models and quick computational techniques. Chapter cover Alternating Diffusion: A Geometric Approach for Sensor Fusion, Generating Structured TV-based Priors and Associated Primal-dual Methods, Graph-based Optimization Approaches for Machine Learning, Uncertainty Quantification and Networks, Extrinsic Shape Analysis from Boundary Representations, Efficient Numerical Methods for Gradient Flows and Phase-field Models, Recent Advances in Denoising

Processing, Analyzing and Learning of Images, Shapes, and Forms: Part 2

Processing, Analyzing and Learning of Images, Shapes, and Forms: Part 2
  • Author : Anonim
  • Publisher : Elsevier
  • Release : 16 October 2019
GET THIS BOOKProcessing, Analyzing and Learning of Images, Shapes, and Forms: Part 2

Processing, Analyzing and Learning of Images, Shapes, and Forms: Part 2, Volume 20, surveys the contemporary developments relating to the analysis and learning of images, shapes and forms, covering mathematical models and quick computational techniques. Chapter cover Alternating Diffusion: A Geometric Approach for Sensor Fusion, Generating Structured TV-based Priors and Associated Primal-dual Methods, Graph-based Optimization Approaches for Machine Learning, Uncertainty Quantification and Networks, Extrinsic Shape Analysis from Boundary Representations, Efficient Numerical Methods for Gradient Flows and Phase-field Models, Recent Advances in Denoising

Geometric Science of Information

Geometric Science of Information
  • Author : Frank Nielsen,Frédéric Barbaresco
  • Publisher : Springer Nature
  • Release : 14 July 2021
GET THIS BOOKGeometric Science of Information

This book constitutes the proceedings of the 5th International Conference on Geometric Science of Information, GSI 2021, held in Paris, France, in July 2021. The 98 papers presented in this volume were carefully reviewed and selected from 125 submissions. They cover all the main topics and highlights in the domain of geometric science of information, including information geometry manifolds of structured data/information and their advanced applications. The papers are organized in the following topics: Probability and statistics on Riemannian Manifolds; sub-Riemannian geometry and

Handbook of Medical Image Computing and Computer Assisted Intervention

Handbook of Medical Image Computing and Computer Assisted Intervention
  • Author : S. Kevin Zhou,Daniel Rueckert,Gabor Fichtinger
  • Publisher : Academic Press
  • Release : 18 October 2019
GET THIS BOOKHandbook of Medical Image Computing and Computer Assisted Intervention

Handbook of Medical Image Computing and Computer Assisted Intervention presents important advanced methods and state-of-the art research in medical image computing and computer assisted intervention, providing a comprehensive reference on current technical approaches and solutions, while also offering proven algorithms for a variety of essential medical imaging applications. This book is written primarily for university researchers, graduate students and professional practitioners (assuming an elementary level of linear algebra, probability and statistics, and signal processing) working on medical image computing and

Computational Retinal Image Analysis

Computational Retinal Image Analysis
  • Author : Emanuele Trucco,Tom MacGillivray,Yanwu Xu
  • Publisher : Academic Press
  • Release : 25 November 2019
GET THIS BOOKComputational Retinal Image Analysis

Computational Retinal Image Analysis: Tools, Applications and Perspectives gives an overview of contemporary retinal image analysis (RIA) in the context of healthcare informatics and artificial intelligence. Specifically, it provides a history of the field, the clinical motivation for RIA, technical foundations (image acquisition modalities, instruments), computational techniques for essential operations, lesion detection (e.g. optic disc in glaucoma, microaneurysms in diabetes) and validation, as well as insights into current investigations drawing from artificial intelligence and big data. This comprehensive reference

Handbook of Variational Methods for Nonlinear Geometric Data

Handbook of Variational Methods for Nonlinear Geometric Data
  • Author : Philipp Grohs,Martin Holler,Andreas Weinmann
  • Publisher : Springer Nature
  • Release : 03 April 2020
GET THIS BOOKHandbook of Variational Methods for Nonlinear Geometric Data

This book covers different, current research directions in the context of variational methods for non-linear geometric data. Each chapter is authored by leading experts in the respective discipline and provides an introduction, an overview and a description of the current state of the art. Non-linear geometric data arises in various applications in science and engineering. Examples of nonlinear data spaces are diverse and include, for instance, nonlinear spaces of matrices, spaces of curves, shapes as well as manifolds of probability

Shape Analysis in Medical Image Analysis

Shape Analysis in Medical Image Analysis
  • Author : Shuo Li,João Manuel R. S. Tavares
  • Publisher : Springer Science & Business Media
  • Release : 28 January 2014
GET THIS BOOKShape Analysis in Medical Image Analysis

This book contains thirteen contributions from invited experts of international recognition addressing important issues in shape analysis in medical image analysis, including techniques for image segmentation, registration, modelling and classification and applications in biology, as well as in cardiac, brain, spine, chest, lung and clinical practice. This volume treats topics such as for example, anatomic and functional shape representation and matching; shape-based medical image segmentation; shape registration; statistical shape analysis; shape deformation; shape-based abnormity detection; shape tracking and longitudinal shape

Riemannian Computing in Computer Vision

Riemannian Computing in Computer Vision
  • Author : Pavan K. Turaga,Anuj Srivastava
  • Publisher : Springer
  • Release : 09 November 2015
GET THIS BOOKRiemannian Computing in Computer Vision

This book presents a comprehensive treatise on Riemannian geometric computations and related statistical inferences in several computer vision problems. This edited volume includes chapter contributions from leading figures in the field of computer vision who are applying Riemannian geometric approaches in problems such as face recognition, activity recognition, object detection, biomedical image analysis, and structure-from-motion. Some of the mathematical entities that necessitate a geometric analysis include rotation matrices (e.g. in modeling camera motion), stick figures (e.g. for activity

Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis

Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis
  • Author : Milan Sonka,Ioannis A. Kakadiaris,Jan Kybic
  • Publisher : Springer Science & Business Media
  • Release : 20 September 2004
GET THIS BOOKComputer Vision and Mathematical Methods in Medical and Biomedical Image Analysis

Medical imaging and medical image analysisare rapidly developing. While m- ical imaging has already become a standard of modern medical care, medical image analysis is still mostly performed visually and qualitatively. The ev- increasing volume of acquired data makes it impossible to utilize them in full. Equally important, the visual approaches to medical image analysis are known to su?er from a lack of reproducibility. A signi?cant researche?ort is devoted to developing algorithms for processing the wealth of