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Riemannian Geometric Statistics in Medical Image Analysis

Riemannian Geometric Statistics in Medical Image Analysis
  • Author : Xavier Pennec
  • Publsiher : Academic Press
  • Release : 02 September 2019
  • ISBN : 0128147261
  • Pages : 636 pages
  • Rating : 4/5 from 21 ratings
GET THIS BOOKRiemannian Geometric Statistics in Medical Image Analysis

Summary:
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. Beyond medical image computing, the methods described in this book may also apply to other domains such as signal processing, computer vision, geometric deep learning, and other domains where statistics on geometric features appear. As such, the presented core methodology takes its place in the field of geometric statistics, the statistical analysis of data being elements of nonlinear geometric spaces. The foundational material and the advanced techniques presented in the later parts of the book can be useful in domains outside medical imaging and present important applications of geometric statistics methodology 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


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


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



Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2010

Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2010
  • Author : Tianzi Jiang,Nassir Navab,Josien P.W. Pluim,Max A. Viergever
  • Publisher : Springer Science & Business Media
  • Release : 01 September 2010
GET THIS BOOKMedical Image Computing and Computer-Assisted Intervention -- MICCAI 2010

The three-volume set LNCS 6361, 6362 and 6363 constitutes the refereed proceedings of the 13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010, held in Beijing, China, in September 2010. Based on rigorous peer reviews, the program committee carefully selected 251 revised papers from 786 submissions for presentation in three volumes. The first volume includes 84 papers organized in topical sections on computer-aided diagnosis, planning and guidance of interventions, image segmentation, image reconstruction and restoration, functional and diffusion-weighted MRI, modeling and simulation, instrument and patient


Statistics and Analysis of Shapes

Statistics and Analysis of Shapes
  • Author : Hamid Krim,Anthony Yezzi
  • Publisher : Springer Science & Business Media
  • Release : 31 December 2007
GET THIS BOOKStatistics and Analysis of Shapes

The subject of pattern analysis and recognition pervades many aspects of our daily lives, including user authentication in banking, object retrieval from databases in the consumer sector, and the omnipresent surveillance and security measures around sensitive areas. Shape analysis, a fundamental building block in many approaches to these applications, is also used in statistics, biomedical applications (Magnetic Resonance Imaging), and many other related disciplines. With contributions from some of the leading experts and pioneers in the field, this self-contained, unified


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



Information Processing in Medical Imaging

Information Processing in Medical Imaging
  • Author : Marc Niethammer,Martin Styner,Stephen Aylward,Hongtu Zhu,Ipek Oguz,Pew-Thian Yap,Dinggang Shen
  • Publisher : Springer
  • Release : 06 June 2017
GET THIS BOOKInformation Processing in Medical Imaging

This book constitutes the proceedings of the 25th International Conference on Information Processing in Medical Imaging, IPMI 2017, held at the Appalachian State University, Boon, NC, USA, in June 2017. The 53 full papers presented in this volume were carefully reviewed and selected from 147 submissions. They were organized in topical sections named: analysis on manifolds; shape analysis; disease diagnosis/progression; brain networks an connectivity; diffusion imaging; quantitative imaging; imaging genomics; image registration; segmentation; general image analysis.


Algorithmic Advances in Riemannian Geometry and Applications

Algorithmic Advances in Riemannian Geometry and Applications
  • Author : Hà Quang Minh,Vittorio Murino
  • Publisher : Springer
  • Release : 05 October 2016
GET THIS BOOKAlgorithmic Advances in Riemannian Geometry and Applications

This book presents a selection of the most recent algorithmic advances in Riemannian geometry in the context of machine learning, statistics, optimization, computer vision, and related fields. The unifying theme of the different chapters in the book is the exploitation of the geometry of data using the mathematical machinery of Riemannian geometry. As demonstrated by all the chapters in the book, when the data is intrinsically non-Euclidean, the utilization of this geometrical information can lead to better algorithms that can



Latent Variable Analysis and Signal Separation

Latent Variable Analysis and Signal Separation
  • Author : Emmanuel Vincent,Arie Yeredor,Zbyněk Koldovský,Petr Tichavský
  • Publisher : Springer
  • Release : 14 August 2015
GET THIS BOOKLatent Variable Analysis and Signal Separation

This book constitutes the proceedings of the 12th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICS 2015, held in Liberec, Czech Republic, in August 2015. The 61 revised full papers presented – 29 accepted as oral presentations and 32 accepted as poster presentations – were carefully reviewed and selected from numerous submissions. Five special topics are addressed: tensor-based methods for blind signal separation; deep neural networks for supervised speech separation/enhancement; joined analysis of multiple datasets, data fusion, and related topics; advances in nonlinear


Medical Image Computing and Computer-Assisted Intervention - MICCAI'98

Medical Image Computing and Computer-Assisted Intervention - MICCAI'98
  • Author : William M. Wells,Alan Colchester,Scott Delp
  • Publisher : Springer Science & Business Media
  • Release : 02 October 1998
GET THIS BOOKMedical Image Computing and Computer-Assisted Intervention - MICCAI'98

This book constitutes the refereed proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI'98, held in Cambridge, MA, USA, in October 1998. The 134 revised papers presented were carefully selected from a total of 243 submissions. The book is divided into topical sections on surgical planning, surgical navigation and measurements, cardiac image analysis, medical robotic systems, surgical systems and simulators, segmentation, computational neuroanatomy, biomechanics, detection in medical images, data acquisition and processing, neurosurgery and neuroscience, shape analysis, feature



Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis

Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis
  • Author : Anonim
  • Publisher : IEEE Computer Society
  • Release : 06 March 1996
GET THIS BOOKProceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis

Thirty-two June 1996 biomedical image workshop papers developing clever computational methods based on geometry, algebra, functional analysis, partial differential equations, optimization and graph theory. Within this mathematical framework the contributors address new and old topics in medical imag


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
  • Release : 04 October 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