Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels.

Journal: Computer methods and programs in biomedicine
Published Date: Jan 11, 2018

Abstract

BACKGROUND: Accurate segmentation of brain tumour in magnetic resonance images (MRI) is a difficult task due to various tumour types. Using information and features from multimodal MRI including structural MRI and isotropic (p) and anisotropic (q) components derived from the diffusion tensor imaging (DTI) may result in a more accurate analysis of brain images.

Authors

  • Mohammadreza Soltaninejad
    School of Computer Science, University of Lincoln, Lincoln LN6 7TS, UK. Electronic address: msoltaninejad@lincoln.ac.uk.
  • Guang Yang
    National Heart and Lung Institute, Imperial College London, London, UK.
  • Tryphon Lambrou
    School of Computer Science, University of Lincoln, Lincoln LN6 7TS, UK. Electronic address: tlambrou@lincoln.ac.uk.
  • Nigel Allinson
    School of Computer Science, University of Lincoln, Lincoln LN6 7TS, UK. Electronic address: nallinson@lincoln.ac.uk.
  • Timothy L Jones
    Academic Neurosurgery Unit, St. George's, University of London, London SW17 0RE, UK. Electronic address: timothy.jones@stgeorges.nhs.uk.
  • Thomas R Barrick
    Neurosciences Research Centre, Molecular and Clinical Sciences Institute, St. George's, University of London, London SW17 0RE, UK. Electronic address: tbarrick@sgul.ac.uk.
  • Franklyn A Howe
    Neurosciences Research Centre, Molecular and Clinical Sciences Institute, St. George's, University of London, London SW17 0RE, UK. Electronic address: howefa@sgul.ac.uk.
  • Xujiong Ye
    School of Computer Science, University of Lincoln, Lincoln LN6 7TS, UK. Electronic address: xye@lincoln.ac.uk.

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