Riemannian Geometric Statistics in Medical Image Analysis

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Xavier Pennec (National Institute for Research in Computer Science and Control, INRIA, Sophia-Antipolis, France) Stefan Sommer (Department of Computer Science, University of Copenhagen)

Riemannian Geometric Statistics in Medical Image Analysis

Xavier Pennec (National Institute for Research in Computer Science and Control, INRIA, Sophia-Antipolis, France), Stefan Sommer (Department of Computer Science, University of Copenhagen), Tom Fletcher (Scientific Computing and Imaging Institute, University of Utah, USA)

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English

Academic Press Inc
04 September 2019

Medical imaging; Differential & Riemannian geometry; Probability & statistics; Computer vision

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.

Edited by: Xavier Pennec (National Institute for Research in Computer Science and Control INRIA Sophia-Antipolis France), Stefan Sommer (Department of Computer Science, University of Copenhagen), Tom Fletcher (Scientific Computing and Imaging Institute, University of Utah, USA)
Imprint: Academic Press Inc
Country of Publication: United States
Dimensions: Height: 235mm, Width: 191mm,
Weight: 1.310kg
ISBN: 9780128147252
ISBN 10: 0128147253
Pages: 636
Publication Date: 04 September 2019
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

Xavier Pennec’s research interest is at the intersection of statistics, differential geometry, computer science and medicine. He is particularly interested in the mathematics involved in computational anatomy: geometric statistics involving statistical computing on Riemannian manifolds and other geometric structures . He has contributed mathematically grounded methods and algorithms for medical image registration, statistics on shapes, and their translation to clinical research applications. Stefan Sommer’s research focus is on modeling and statistics of non-linear data with application to shape spaces, functional data analysis, and image registration. This includes foundational and algorithmic aspects of statistics on manifold valued data, and computational modeling and statistical analysis of deformations occurring in computational anatomy. Tom Fletcher’s research focus is on solving problems in medical image analysis and computer vision through the combination of statistics and differential geometry