Riemannian Geometric Statistics in Medical Image Analysis

Riemannian Geometric Statistics in Medical Image Analysis
Author: Xavier Pennec,Stefan Sommer,Tom Fletcher
Publsiher: Academic Press
Total Pages: 636
Release: 2019-09-02
ISBN 10: 0128147261
ISBN 13: 9780128147269
Language: EN, FR, DE, ES & NL

Riemannian Geometric Statistics in Medical Image Analysis Book Review:

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

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
Publsiher: Academic Press
Total Pages: 1072
Release: 2019-10-18
ISBN 10: 0128165863
ISBN 13: 9780128165867
Language: EN, FR, DE, ES & NL

Handbook of Medical Image Computing and Computer Assisted Intervention Book Review:

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 computer assisted intervention. Presents the key research challenges in medical image computing and computer-assisted intervention Written by leading authorities of the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society Contains state-of-the-art technical approaches to key challenges Demonstrates proven algorithms for a whole range of essential medical imaging applications Includes source codes for use in a plug-and-play manner Embraces future directions in the fields of medical image computing and computer-assisted intervention

Shape in Medical Imaging

Shape in Medical Imaging
Author: Martin Reuter
Publsiher: Springer Nature
Total Pages: 329
Release:
ISBN 10: 303061056X
ISBN 13: 9783030610562
Language: EN, FR, DE, ES & NL

Shape in Medical Imaging Book Review:

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
Publsiher: Springer Science & Business Media
Total Pages: 709
Release: 2010-09
ISBN 10: 3642157041
ISBN 13: 9783642157042
Language: EN, FR, DE, ES & NL

Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2010 Book Review:

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 localization and tracking, quantitative image analysis, image registration, computational and interventional cardiology, and diffusion tensor MR imaging and analysis.

Statistics and Analysis of Shapes

Statistics and Analysis of Shapes
Author: Hamid Krim,Anthony Yezzi
Publsiher: Springer Science & Business Media
Total Pages: 396
Release: 2007-12-31
ISBN 10: 9780817644819
ISBN 13: 0817644814
Language: EN, FR, DE, ES & NL

Statistics and Analysis of Shapes Book Review:

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 volume is the first comprehensive treatment of theory, methods, and algorithms available in a single resource. Developments are discussed from a rapidly increasing number of research papers in diverse fields, including the mathematical and physical sciences, engineering, and medicine.

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016
Author: Sebastien Ourselin,Leo Joskowicz,Mert R. Sabuncu,Gozde Unal,William Wells
Publsiher: Springer
Total Pages: 681
Release: 2016-10-17
ISBN 10: 3319467204
ISBN 13: 9783319467207
Language: EN, FR, DE, ES & NL

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016 Book Review:

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.

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
Publsiher: Springer
Total Pages: 687
Release: 2017-06-06
ISBN 10: 3319590502
ISBN 13: 9783319590509
Language: EN, FR, DE, ES & NL

Information Processing in Medical Imaging Book Review:

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.

Riemannian Computing in Computer Vision

Riemannian Computing in Computer Vision
Author: Pavan K. Turaga,Anuj Srivastava
Publsiher: Springer
Total Pages: 391
Release: 2015-11-09
ISBN 10: 3319229575
ISBN 13: 9783319229577
Language: EN, FR, DE, ES & NL

Riemannian Computing in Computer Vision Book Review:

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 recognition), subspace comparisons (e.g. in face recognition), symmetric positive-definite matrices (e.g. in diffusion tensor imaging), and function-spaces (e.g. in studying shapes of closed contours).

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
Publsiher: Springer Science & Business Media
Total Pages: 444
Release: 2004-09-20
ISBN 10: 3540226753
ISBN 13: 9783540226758
Language: EN, FR, DE, ES & NL

Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis Book Review:

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 data available and extracting the relevant information in a computerized and quantitative fashion. Medical imaging and image analysis are interdisciplinary areas combining electrical, computer, and biomedical engineering; computer science; mathem- ics; physics; statistics; biology; medicine; and other ?elds. Medical imaging and computer vision, interestingly enough, have developed and continue developing somewhat independently. Nevertheless, bringing them together promises to b- e?t both of these ?elds. We were enthusiastic when the organizers of the 2004 European Conference on Computer Vision (ECCV) allowed us to organize a satellite workshop devoted to medical image analysis.

Statistics of Medical Imaging

Statistics of Medical Imaging
Author: Tianhu Lei
Publsiher: CRC Press
Total Pages: 438
Release: 2011-12-19
ISBN 10: 1420088432
ISBN 13: 9781420088434
Language: EN, FR, DE, ES & NL

Statistics of Medical Imaging Book Review:

Statistical investigation into technology not only provides a better understanding of the intrinsic features of the technology (analysis), but also leads to an improved design of the technology (synthesis). Physical principles and mathematical procedures of medical imaging technologies have been extensively studied during past decades. However, less work has been done on the statistical aspects of these techniques. Statistics of Medical Imaging fills this gap and provides a theoretical framework for statistical investigation into medical imaging technologies. Features Describes physical principles and mathematical procedures of two medical imaging techniques: X-ray CT and MRI Presents statistical properties of imaging data (measurements) at each stage in the imaging processes of X-ray CT and MRI Demonstrates image reconstruction as a transform from a set of random variables (imaging data) to another set of random variables (image data) Presents statistical properties of image data (pixel intensities) at three levels: a single pixel, any two pixels, and a group of pixels (a region) Provides two stochastic models for X-ray CT and MR image in terms of their statistics and two model-based statistical image analysis methods Evaluates statistical image analysis methods in terms of their detection, estimation, and classification performances Indicates that X-ray CT, MRI, PET and SPECT belong to a category of imaging: the non-diffraction computed tomography Rather than offering detailed descriptions of statistics of basic imaging protocols of X-ray CT and MRI, this book provides a method to conduct similar statistical investigations into more complicated imaging protocols.

Latent Variable Analysis and Signal Separation

Latent Variable Analysis and Signal Separation
Author: Vincent Vigneron,Vicente Zarzoso,Eric Moreau,Rémi Gribonval,Emmanuel Vincent
Publsiher: Springer Science & Business Media
Total Pages: 655
Release: 2010-09-27
ISBN 10: 364215994X
ISBN 13: 9783642159947
Language: EN, FR, DE, ES & NL

Latent Variable Analysis and Signal Separation Book Review:

Thisvolumecollectsthepaperspresentedatthe9thInternationalConferenceon Latent Variable Analysis and Signal Separation,LVA/ICA 2010. The conference was organized by INRIA, the French National Institute for Computer Science and Control,and was held in Saint-Malo, France, September 27–30,2010,at the Palais du Grand Large. Tenyearsafterthe?rstworkshoponIndependent Component Analysis(ICA) in Aussois, France, the series of ICA conferences has shown the liveliness of the community of theoreticians and practitioners working in this ?eld. While ICA and blind signal separation have become mainstream topics, new approaches have emerged to solve problems involving signal mixtures or various other types of latent variables: semi-blind models, matrix factorization using sparse com- nent analysis, non-negative matrix factorization, probabilistic latent semantic indexing, tensor decompositions, independent vector analysis, independent s- space analysis, and so on. To re?ect this evolution towards more general latent variable analysis problems in signal processing, the ICA International Steering Committee decided to rename the 9th instance of the conference LVA/ICA. From more than a hundred submitted papers, 25 were accepted as oral p- sentationsand53 asposter presentations. Thecontent ofthis volumefollowsthe conference schedule, resulting in 14 chapters. The papers collected in this v- ume demonstrate that the research activity in the ?eld continues to range from abstract concepts to the most concrete and applicable questions and consid- ations. Speech and audio, as well as biomedical applications, continue to carry the mass of the applications considered.

Algorithmic Advances in Riemannian Geometry and Applications

Algorithmic Advances in Riemannian Geometry and Applications
Author: Hà Quang Minh,Vittorio Murino
Publsiher: Springer
Total Pages: 208
Release: 2016-10-05
ISBN 10: 3319450263
ISBN 13: 9783319450261
Language: EN, FR, DE, ES & NL

Algorithmic Advances in Riemannian Geometry and Applications Book Review:

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 capture more accurately the structures inherent in the data, leading ultimately to better empirical performance. This book is not intended to be an encyclopedic compilation of the applications of Riemannian geometry. Instead, it focuses on several important research directions that are currently actively pursued by researchers in the field. These include statistical modeling and analysis on manifolds,optimization on manifolds, Riemannian manifolds and kernel methods, and dictionary learning and sparse coding on manifolds. Examples of applications include novel algorithms for Monte Carlo sampling and Gaussian Mixture Model fitting, 3D brain image analysis,image classification, action recognition, and motion tracking.

Nonparametric Statistics on Manifolds and Their Applications to Object Data Analysis

Nonparametric Statistics on Manifolds and Their Applications to Object Data Analysis
Author: Victor Patrangenaru,Leif Ellingson
Publsiher: CRC Press
Total Pages: 517
Release: 2015-09-18
ISBN 10: 1439820511
ISBN 13: 9781439820513
Language: EN, FR, DE, ES & NL

Nonparametric Statistics on Manifolds and Their Applications to Object Data Analysis Book Review:

A New Way of Analyzing Object Data from a Nonparametric Viewpoint Nonparametric Statistics on Manifolds and Their Applications to Object Data Analysis provides one of the first thorough treatments of the theory and methodology for analyzing data on manifolds. It also presents in-depth applications to practical problems arising in a variety of fields, including statistics, medical imaging, computer vision, pattern recognition, and bioinformatics. The book begins with a survey of illustrative examples of object data before moving to a review of concepts from mathematical statistics, differential geometry, and topology. The authors next describe theory and methods for working on various manifolds, giving a historical perspective of concepts from mathematics and statistics. They then present problems from a wide variety of areas, including diffusion tensor imaging, similarity shape analysis, directional data analysis, and projective shape analysis for machine vision. The book concludes with a discussion of current related research and graduate-level teaching topics as well as considerations related to computational statistics. Researchers in diverse fields must combine statistical methodology with concepts from projective geometry, differential geometry, and topology to analyze data objects arising from non-Euclidean object spaces. An expert-driven guide to this approach, this book covers the general nonparametric theory for analyzing data on manifolds, methods for working with specific spaces, and extensive applications to practical research problems. These problems show how object data analysis opens a formidable door to the realm of big data analysis.

Riemannian Geometry and Geometric Analysis

Riemannian Geometry and Geometric Analysis
Author: Jürgen Jost
Publsiher: Springer Science & Business Media
Total Pages: 566
Release: 2006-03-30
ISBN 10: 3540288910
ISBN 13: 9783540288916
Language: EN, FR, DE, ES & NL

Riemannian Geometry and Geometric Analysis Book Review:

Offering some of the topics of contemporary mathematical research, this fourth edition includes a systematic introduction to Kahler geometry and the presentation of additional techniques from geometric analysis.

Guide to Medical Image Analysis

Guide to Medical Image Analysis
Author: Klaus D. Toennies
Publsiher: Springer
Total Pages: 589
Release: 2017-03-29
ISBN 10: 1447173201
ISBN 13: 9781447173205
Language: EN, FR, DE, ES & NL

Guide to Medical Image Analysis Book Review:

This comprehensive guide provides a uniquely practical, application-focused introduction to medical image analysis. This fully updated new edition has been enhanced with material on the latest developments in the field, whilst retaining the original focus on segmentation, classification and registration. Topics and features: presents learning objectives, exercises and concluding remarks in each chapter; describes a range of common imaging techniques, reconstruction techniques and image artifacts, and discusses the archival and transfer of images; reviews an expanded selection of techniques for image enhancement, feature detection, feature generation, segmentation, registration, and validation; examines analysis methods in view of image-based guidance in the operating room (NEW); discusses the use of deep convolutional networks for segmentation and labeling tasks (NEW); includes appendices on Markov random field optimization, variational calculus and principal component analysis.

Geometric Methods in Signal and Image Analysis

Geometric Methods in Signal and Image Analysis
Author: Hamid Krim,Abdessamad Ben Hamza
Publsiher: Cambridge University Press
Total Pages: 295
Release: 2015-06-18
ISBN 10: 110703390X
ISBN 13: 9781107033900
Language: EN, FR, DE, ES & NL

Geometric Methods in Signal and Image Analysis Book Review:

A comprehensive guide to modern geometric methods for signal and image analysis, from basic principles to state-of-the-art concepts and applications.

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
Publsiher: Springer Science & Business Media
Total Pages: 1258
Release: 1998-10-02
ISBN 10: 9783540651369
ISBN 13: 3540651365
Language: EN, FR, DE, ES & NL

Medical Image Computing and Computer-Assisted Intervention - MICCAI'98 Book Review:

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 extraction, registration, and ultrasound.

Information Processing in Medical Imaging

Information Processing in Medical Imaging
Author: Albert C. S. Chung,James C. Gee,Paul A. Yushkevich,Siqi Bao
Publsiher: Springer
Total Pages: 884
Release: 2019-05-22
ISBN 10: 3030203514
ISBN 13: 9783030203511
Language: EN, FR, DE, ES & NL

Information Processing in Medical Imaging Book Review:

This book constitutes the proceedings of the 26th International Conference on Information Processing in Medical Imaging, IPMI 2019, held at the Hong Kong University of Science and Technology, Hong Kong, China, in June 2019. The 69 full papers presented in this volume were carefully reviewed and selected from 229 submissions. They were organized in topical sections on deep learning and segmentation; classification and inference; reconstruction; disease modeling; shape, registration; learning motion; functional imaging; and white matter imaging. The book also includes a number of post papers.

Variational Methods

Variational Methods
Author: Maitine Bergounioux,Gabriel Peyré,Christoph Schnörr,Jean-Baptiste Caillau,Thomas Haberkorn
Publsiher: Walter de Gruyter GmbH & Co KG
Total Pages: 540
Release: 2017-01-11
ISBN 10: 3110430398
ISBN 13: 9783110430394
Language: EN, FR, DE, ES & NL

Variational Methods Book Review:

With a focus on the interplay between mathematics and applications of imaging, the first part covers topics from optimization, inverse problems and shape spaces to computer vision and computational anatomy. The second part is geared towards geometric control and related topics, including Riemannian geometry, celestial mechanics and quantum control. Contents: Part I Second-order decomposition model for image processing: numerical experimentation Optimizing spatial and tonal data for PDE-based inpainting Image registration using phase・amplitude separation Rotation invariance in exemplar-based image inpainting Convective regularization for optical flow A variational method for quantitative photoacoustic tomography with piecewise constant coefficients On optical flow models for variational motion estimation Bilevel approaches for learning of variational imaging models Part II Non-degenerate forms of the generalized Euler・Lagrange condition for state-constrained optimal control problems The Purcell three-link swimmer: some geometric and numerical aspects related to periodic optimal controls Controllability of Keplerian motion with low-thrust control systems Higher variational equation techniques for the integrability of homogeneous potentials Introduction to KAM theory with a view to celestial mechanics Invariants of contact sub-pseudo-Riemannian structures and Einstein・Weyl geometry Time-optimal control for a perturbed Brockett integrator Twist maps and Arnold diffusion for diffeomorphisms A Hamiltonian approach to sufficiency in optimal control with minimal regularity conditions: Part I Index

Medical Image Registration

Medical Image Registration
Author: Joseph V. Hajnal,Derek L.G. Hill
Publsiher: CRC Press
Total Pages: 392
Release: 2001-06-27
ISBN 10: 9781420042474
ISBN 13: 1420042475
Language: EN, FR, DE, ES & NL

Medical Image Registration Book Review:

Image registration is the process of systematically placing separate images in a common frame of reference so that the information they contain can be optimally integrated or compared. This is becoming the central tool for image analysis, understanding, and visualization in both medical and scientific applications. Medical Image Registration provid