Riemannian Geometric Statistics in Medical Image Analysis

Publikation: Bog/antologi/afhandling/rapportBogForskningfagfællebedømt

Standard

Riemannian Geometric Statistics in Medical Image Analysis. / Pennec, Xavier (Redaktør); Sommer, Stefan Horst (Redaktør); Fletcher, Tom (Redaktør).

1. udg. Academic Press, 2020. 636 s.

Publikation: Bog/antologi/afhandling/rapportBogForskningfagfællebedømt

Harvard

Pennec, X, Sommer, SH & Fletcher, T (red) 2020, Riemannian Geometric Statistics in Medical Image Analysis. 1. udg, Academic Press. https://doi.org/10.1016/C2017-0-01561-6

APA

Pennec, X., Sommer, S. H., & Fletcher, T. (red.) (2020). Riemannian Geometric Statistics in Medical Image Analysis. (1. udg.) Academic Press. https://doi.org/10.1016/C2017-0-01561-6

Vancouver

Pennec X, (ed.), Sommer SH, (ed.), Fletcher T, (ed.). Riemannian Geometric Statistics in Medical Image Analysis. 1. udg. Academic Press, 2020. 636 s. https://doi.org/10.1016/C2017-0-01561-6

Author

Pennec, Xavier (Redaktør) ; Sommer, Stefan Horst (Redaktør) ; Fletcher, Tom (Redaktør). / Riemannian Geometric Statistics in Medical Image Analysis. 1. udg. Academic Press, 2020. 636 s.

Bibtex

@book{ff5d0f0a23c84a18a4cf60bdf8efe1aa,
title = "Riemannian Geometric Statistics in Medical Image Analysis",
abstract = "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.",
editor = "Xavier Pennec and Sommer, {Stefan Horst} and Tom Fletcher",
year = "2020",
doi = "10.1016/C2017-0-01561-6",
language = "English",
isbn = "9780128147252",
publisher = "Academic Press",
address = "United States",
edition = "1.",

}

RIS

TY - BOOK

T1 - Riemannian Geometric Statistics in Medical Image Analysis

A2 - Pennec, Xavier

A2 - Sommer, Stefan Horst

A2 - Fletcher, Tom

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

UR - https://www.elsevier.com/books/riemannian-geometric-statistics-in-medical-image-analysis/pennec/978-0-12-814725-2

U2 - 10.1016/C2017-0-01561-6

DO - 10.1016/C2017-0-01561-6

M3 - Book

SN - 9780128147252

BT - Riemannian Geometric Statistics in Medical Image Analysis

PB - Academic Press

ER -

ID: 231753617