EbookBell.com

Most ebook files are in PDF format, so you can easily read them using various software such as Foxit Reader or directly on the Google Chrome browser.
Some ebook files are released by publishers in other formats such as .awz, .mobi, .epub, .fb2, etc. You may need to install specific software to read these formats on mobile/PC, such as Calibre.

Please read the tutorial at this link: https://ebookbell.com/faq

We offer FREE conversion to the popular formats you request; however, this may take some time. Therefore, right after payment, please email us, and we will try to provide the service as quickly as possible.

For some exceptional file formats or broken links (if any), please refrain from opening any disputes. Instead, email us first, and we will try to assist within a maximum of 6 hours.

EbookBell Team

Riemannian Geometric Statistics In Medical Image Analysis 1st Edition Xavier Pennec

Name: Riemannian Geometric Statistics In Medical Image Analysis 1st Edition Xavier Pennec
SKU: BELL-10666136
Price: 31.00 USD
Availability: InStock
Rating: 5 (78 reviews)

SKU: BELL-10666136

$ 31.00 ~~$ 45.00~~ (-31%)

5.0

78 reviews

Riemannian Geometric Statistics In Medical Image Analysis 1st Edition Xavier Pennec instant download after payment.

Publisher: Academic Press

File Extension: PDF

File size: 24.76 MB

Pages: 637

Author: Xavier Pennec, Stefan Sommer, Tom Fletcher

ISBN: 9780128147252, 0128147253

Language: English

Year: 2019

Edition: 1

Product desciption

Riemannian Geometric Statistics In Medical Image Analysis 1st Edition Xavier Pennec by Xavier Pennec, Stefan Sommer, Tom Fletcher 9780128147252, 0128147253 instant download after payment.

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.