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Riemannian Geometric Statistics in Medical Image Analysis

Riemannian Geometric Statistics in Medical Image Analysis
  • Author : Xavier Pennec,Stefan Sommer,Tom Fletcher
  • Publisher :Unknown
  • Release Date :2019-09-02
  • Total pages :636
  • ISBN : 9780128147269
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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

Shape in Medical Imaging

Shape in Medical Imaging
  • Author : Martin Reuter
  • Publisher :Unknown
  • Release Date :2021
  • Total pages :229
  • ISBN : 9783030610562
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Summary :

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 :Unknown
  • Release Date :2019-10-18
  • Total pages :1072
  • ISBN : 9780128165867
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Summary : 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

Computational Retinal Image Analysis

Computational Retinal Image Analysis
  • Author : Emanuele Trucco,Tom MacGillivray,Yanwu Xu
  • Publisher :Unknown
  • Release Date :2019-11-25
  • Total pages :502
  • ISBN : 9780081028179
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Summary : 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 is ideal for researchers and graduate students in retinal image analysis, computational ophthalmology, artificial intelligence, biomedical engineering, health informatics, and more. Provides a unique, well-structured and integrated overview of retinal image analysis Gives insights into future areas, such as large-scale screening programs, precision medicine, and computer-assisted eye care Includes plans and aspirations of companies and professional bodies

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 :Unknown
  • Release Date :2017-06-06
  • Total pages :687
  • ISBN : 9783319590509
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Summary : 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.

Dissertation Abstracts International

Dissertation Abstracts International
  • Author : Anonim
  • Publisher :Unknown
  • Release Date :2006
  • Total pages :229
  • ISBN : STANFORD:36105123430014
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Summary :

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 :Unknown
  • Release Date :1996
  • Total pages :340
  • ISBN : 0818673672
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Summary : 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

Algorithmic Advances in Riemannian Geometry and Applications

Algorithmic Advances in Riemannian Geometry and Applications
  • Author : Hà Quang Minh,Vittorio Murino
  • Publisher :Unknown
  • Release Date :2016-10-05
  • Total pages :208
  • ISBN : 9783319450261
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Summary : 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.

Medical Image Computing and Computer-assisted Intervention

Medical Image Computing and Computer-assisted Intervention
  • Author : Anonim
  • Publisher :Unknown
  • Release Date :1998
  • Total pages :229
  • ISBN : UOM:39015048112463
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Summary :

Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008

Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008
  • Author : Dimitris Metaxas,Leon Axel,Gabor Fichtinger,Gabor Szekely
  • Publisher :Unknown
  • Release Date :2008-09-04
  • Total pages :1087
  • ISBN : 9783540859871
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Summary : The two-volume set LNCS 5241 and LNCS 5242 constitute the refereed proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2008, held in New York, NY, USA, in September 2008. The program committee carefully selected 258 revised papers from numerous submissions for presentation in two volumes, based on rigorous peer reviews. The first volume includes 127 papers related to medical image computing, segmentation, shape and statistics analysis, modeling, motion tracking and compensation, as well as registration. The second volume contains 131 contributions related to robotics and interventions, statistical analysis, segmentation, intervention, modeling, and registration.

Medical Image Registration

Medical Image Registration
  • Author : Joseph V. Hajnal,Derek L.G. Hill
  • Publisher :Unknown
  • Release Date :2001-06-27
  • Total pages :392
  • ISBN : 1420042475
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Summary : 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

Latent Variable Analysis and Signal Separation

Latent Variable Analysis and Signal Separation
  • Author : Emmanuel Vincent,Arie Yeredor,Zbyněk Koldovský,Petr Tichavský
  • Publisher :Unknown
  • Release Date :2015-08-14
  • Total pages :532
  • ISBN : 9783319224824
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Summary : 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 blind source separation; sparse and low rank modeling for acoustic signal processing.

Mathematical Reviews

Mathematical Reviews
  • Author : Anonim
  • Publisher :Unknown
  • Release Date :2007
  • Total pages :229
  • ISBN : UOM:39015078588624
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Summary :

Medical Imaging

Medical Imaging
  • Author : Anonim
  • Publisher :Unknown
  • Release Date :2004
  • Total pages :229
  • ISBN : UOM:39015058295612
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Summary :

Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008

Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008
  • Author : Dimitris Metaxas,Leon Axel,Gabor Fichtinger,Gabor Szekely
  • Publisher :Unknown
  • Release Date :2008-09-04
  • Total pages :1087
  • ISBN : 9783540859871
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Summary : The two-volume set LNCS 5241 and LNCS 5242 constitute the refereed proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2008, held in New York, NY, USA, in September 2008. The program committee carefully selected 258 revised papers from numerous submissions for presentation in two volumes, based on rigorous peer reviews. The first volume includes 127 papers related to medical image computing, segmentation, shape and statistics analysis, modeling, motion tracking and compensation, as well as registration. The second volume contains 131 contributions related to robotics and interventions, statistical analysis, segmentation, intervention, modeling, and registration.

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 :Unknown
  • Release Date :2004-10-04
  • Total pages :444
  • ISBN : 9783540278160
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Summary : 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.

Elastic Shape Analysis of Three-Dimensional Objects

Elastic Shape Analysis of Three-Dimensional Objects
  • Author : Ian H. Jermyn,Sebastian Kurtek,Hamid Laga,Anuj Srivastava
  • Publisher :Unknown
  • Release Date :2017-09-15
  • Total pages :185
  • ISBN : 9781681730288
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Summary : Statistical analysis of shapes of 3D objects is an important problem with a wide range of applications. This analysis is difficult for many reasons, including the fact that objects differ in both geometry and topology. In this manuscript, we narrow the problem by focusing on objects with fixed topology, say objects that are diffeomorphic to unit spheres, and develop tools for analyzing their geometries. The main challenges in this problem are to register points across objects and to perform analysis while being invariant to certain shape-preserving transformations. We develop a comprehensive framework for analyzing shapes of spherical objects, i.e., objects that are embeddings of a unit sphere in R, including tools for: quantifying shape differences, optimally deforming shapes into each other, summarizing shape samples, extracting principal modes of shape variability, and modeling shape variability associated with populations. An important strength of this framework is that it is elastic: it performs alignment, registration, and comparison in a single unified framework, while being invariant to shape-preserving transformations. The approach is essentially Riemannian in the following sense. We specify natural mathematical representations of surfaces of interest, and impose Riemannian metrics that are invariant to the actions of the shape-preserving transformations. In particular, they are invariant to reparameterizations of surfaces. While these metrics are too complicated to allow broad usage in practical applications, we introduce a novel representation, termed square-root normal fields (SRNFs), that transform a particular invariant elastic metric into the standard L2 metric. As a result, one can use standard techniques from functional data analysis for registering, comparing, and summarizing shapes. Specifically, this results in: pairwise registration of surfaces; computation of geodesic paths encoding optimal deformations; computation of Karcher means and covariances under the shape metric; tangent Principal Component Analysis (PCA) and extraction of dominant modes of variability; and finally, modeling of shape variability using wrapped normal densities. These ideas are demonstrated using two case studies: the analysis of surfaces denoting human bodies in terms of shape and pose variability; and the clustering and classification of the shapes of subcortical brain structures for use in medical diagnosis. This book develops these ideas without assuming advanced knowledge in differential geometry and statistics. We summarize some basic tools from differential geometry in the appendices, and introduce additional concepts and terminology as needed in the individual chapters.

Riemannian Geometry and Geometric Analysis

Riemannian Geometry and Geometric Analysis
  • Author : Jürgen Jost
  • Publisher :Unknown
  • Release Date :2013-03-09
  • Total pages :535
  • ISBN : 9783662046722
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Summary : This established reference work continues to lead its readers to some of the hottest topics of contemporary mathematical research. This third edition includes a new presentation of Morse theory and Floer homology. The new material emphasises the geometric aspects and is discussed in the context of Riemannian geometry and geometric analysis. The book also now covers the geometric aspects of harmonic maps, using geometric methods from the theory of geometric spaces of nonpositive curvature. The new material is based on a course at the University of Leipzig. The text is aimed at graduate students and researchers from other areas of mathematics.

Proceedings

Proceedings
  • Author : Anonim
  • Publisher :Unknown
  • Release Date :2003
  • Total pages :1530
  • ISBN : 0769519504
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Information Processing in Medical Imaging

Information Processing in Medical Imaging
  • Author : Anonim
  • Publisher :Unknown
  • Release Date :1997
  • Total pages :229
  • ISBN : UOM:39015035285348
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Summary :

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
  • Publisher :Unknown
  • Release Date :2015-09-18
  • Total pages :517
  • ISBN : 9781439820513
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Summary : 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.