Riemannian Geometric Statistics in Medical Image Analysis
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AlbakiReads Editorial Geometric statistics for nonlinear medical-image data
Geometric statistics for nonlinear medical-image data
Edited by Xavier Pennec, this 636-page reference develops statistical methods for data that lie on Riemannian manifolds and other nonlinear geometric spaces. It begins with foundational concepts, emphasizing methodology over mathematical proofs, then moves into advanced approaches used in medical image computing. Coverage includes manifold and shape-space statistics as well as diffeomorphic deformations and their applications. Although medical imaging anchors the volume, its framework also extends to geometric features encountered in computer vision, signal processing, and geometric deep learning. The book positions Riemannian geometry as a computational and statistical toolkit for analyzing structures that are not well represented by conventional linear data models.
Good fit for readers who enjoy
- Graduate students studying medical imaging, engineering, or computer science
- Researchers working with manifold-valued data and shape analysis
- Practitioners exploring geometric methods in computer vision, signal processing, or geometric deep learning
Themes
Riemannian manifoldsGeometric statisticsMedical image analysisShape spacesDiffeomorphic deformations
Based on publisher information and book metadata.
Book Overview Over the past 15 years, there has been a growing need in the medical image computing community for principled methods to process nonlinea...
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.
Book Details Format: Paperback | Pages: 636 | Language: English | Publisher: ACADEMIC PR INC | ISBN: 0128147253
FormatPaperback
Pages636
LanguageEnglish
ISBN0128147253
EAN9780128147252
PublisherACADEMIC PR INC
Publication Date2019-09-04
Edition1
AccessoriesNo Accessory
ConditionNew
Product TypeQUALITY PAPERBACK BOOKS
Weight2.37 Pounds
Length9.25 Inches
Width7.5 Inches
Height1.28 Inches
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