Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI

Soltaninejad, M. orcid.org/0000-0002-9889-4369, Yang, G. orcid.org/0000-0001-7344-7733, Lambrou, T. orcid.org/0000-0003-2899-5815 et al. (5 more authors) (2017) Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI. International Journal of Computer Assisted Radiology and Surgery, 12 (2). pp. 183-203. ISSN 1861-6410

Abstract

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Authors/Creators:
Copyright, Publisher and Additional Information: © The Author(s) 2016. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Keywords: Brain tumour segmentation; Extremely randomized trees; Feature selection; Magnetic resonance imaging; Superpixels; Textons; Adult; Aged; Brain; Brain Neoplasms; Female; Glioma; Humans; Image Processing, Computer-Assisted; Magnetic Resonance Imaging; Male; Middle Aged; Reproducibility of Results; Support Vector Machine; Young Adult
Dates:
  • Accepted: 31 August 2016
  • Published (online): 20 September 2016
  • Published: February 2017
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 16 Feb 2018 16:50
Last Modified: 16 Feb 2018 16:50
Published Version: https://doi.org/10.1007/s11548-016-1483-3
Status: Published
Publisher: Springer Verlag
Refereed: Yes
Identification Number: https://doi.org/10.1007/s11548-016-1483-3
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