Riemannian Geometric Statistics in Medical Image Analysis
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Sprache:Englisch
109,00 €
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Produktdetails
Format
ePUB 3
Kopierschutz
Nein
Family Sharing
Nein
Text-to-Speech
Ja
Erscheinungsdatum
02.09.2019
Herausgeber
Xavier Pennec + weitereVerlag
Elsevier Science & Techn.Seitenzahl
636 (Printausgabe)
Sprache
Englisch
EAN
9780128147269
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 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
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