Improved identification of abdominal aortic aneurysm using the Kernelized Expectation Maximization algorithm

Deidda, D, Akerele, MI, Aykroyd, RG orcid.org/0000-0003-3700-0816 et al. (7 more authors) (2021) Improved identification of abdominal aortic aneurysm using the Kernelized Expectation Maximization algorithm. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 379 (2200). 20200201. ISSN 1364-503X

Abstract

Metadata

Authors/Creators:
Copyright, Publisher and Additional Information: © 2021 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
Keywords: PET; aortic aneurysm; PET/CT; kernel method
Dates:
  • Published: 28 June 2021
  • Accepted: 16 October 2020
  • Published (online): 10 May 2021
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds)
The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Leeds Institute of Cardiovascular and Metabolic Medicine (LICAMM) > Biomedical Imaging Science Dept (Leeds)
Funding Information:
FunderGrant number
Royal SocietyIF170011
EPSRC (Engineering and Physical Sciences Research Council)Not Known
Depositing User: Symplectic Publications
Date Deposited: 20 Oct 2020 11:57
Last Modified: 25 Jun 2021 06:06
Status: Published
Publisher: The Royal Society
Identification Number: https://doi.org/10.1098/rsta.2020.0201

Download

Share / Export

Statistics