title: Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI.
abstract: OBJECTIVE
  We propose a fully automated method for detection and segmentation of the abnormal tissue associated with brain tumour (tumour core and oedema) from Fluid- Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Imaging (MRI).
  
  
  METHODS
  The method is based on superpixel technique and classification of each superpixel. A number of novel image features including intensity-based, Gabor textons, fractal analysis and curvatures are calculated from each superpixel within the entire brain area in FLAIR MRI to ensure a robust classification. Extremely randomized trees (ERT) classifier is compared with support vector machine (SVM) to classify each superpixel into tumour and non-tumour.
  
  
  RESULTS
  The proposed method is evaluated on two datasets: (1) Our own clinical dataset: 19 MRI FLAIR images of patients with gliomas of grade II to IV, and (2) BRATS 2012 dataset: 30 FLAIR images with 10 low-grade and 20 high-grade gliomas. The experimental results demonstrate the high detection and segmentation performance of the proposed method using ERT classifier. For our own cohort, the average detection sensitivity, balanced error rate and the Dice overlap measure for the segmented tumour against the ground truth are 89.48 %, 6 % and 0.91, respectively, while, for the BRATS dataset, the corresponding evaluation results are 88.09 %, 6 % and 0.88, respectively.
  
  
  CONCLUSIONS
  This provides a close match to expert delineation across all grades of glioma, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management.
authors.affiliation: Laboratory of Vision Engineering, School of Computer Science, University of Lincoln, Lincoln, LN6 7TS, UK. msoltaninejad@lincoln.ac.uk.
authors.forename: Mohammadreza
authors.identifier: http://orcid.org/0000-0002-9889-4369
authors.initials: M
authors.lastname: Soltaninejad
authors.affiliation: Neurosciences Research Centre, Molecular and Clinical Sciences Institute, St. George's, University of London, London, SW17 0RE, UK.|National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK.
authors.forename: Guang
authors.identifier: http://orcid.org/0000-0001-7344-7733
authors.initials: G
authors.lastname: Yang
authors.affiliation: Laboratory of Vision Engineering, School of Computer Science, University of Lincoln, Lincoln, LN6 7TS, UK.
authors.forename: Tryphon
authors.identifier: http://orcid.org/0000-0003-2899-5815
authors.initials: T
authors.lastname: Lambrou
authors.affiliation: Laboratory of Vision Engineering, School of Computer Science, University of Lincoln, Lincoln, LN6 7TS, UK.
authors.forename: Nigel
authors.identifier: http://orcid.org/0000-0002-4775-8332
authors.initials: N
authors.lastname: Allinson
authors.affiliation: Atkinson Morley Department of Neurosurgery, St George's Hospital London, London, SW17 0RE, UK.
authors.forename: Timothy L
authors.initials: TL
authors.lastname: Jones
authors.affiliation: Neurosciences Research Centre, Molecular and Clinical Sciences Institute, St. George's, University of London, London, SW17 0RE, UK.
authors.forename: Thomas R
authors.initials: TR
authors.lastname: Barrick
authors.affiliation: Neurosciences Research Centre, Molecular and Clinical Sciences Institute, St. George's, University of London, London, SW17 0RE, UK.
authors.forename: Franklyn A
authors.initials: FA
authors.lastname: Howe
authors.affiliation: Laboratory of Vision Engineering, School of Computer Science, University of Lincoln, Lincoln, LN6 7TS, UK.
authors.forename: Xujiong
authors.identifier: http://orcid.org/0000-0003-0115-0724
authors.initials: X
authors.lastname: Ye
mesh_terms: D000328:Adult; D000368:Aged; D001921:Brain / Q000000981:diagnostic imaging; D001932:Brain Neoplasms / Q000000981:diagnostic imaging*; D005260:Female; D005910:Glioma / Q000000981:diagnostic imaging*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D015203:Reproducibility of Results; D060388:Support Vector Machine; D055815:Young Adult
keywords: Brain tumour segmentation; Extremely randomized trees; Feature selection; Magnetic resonance imaging; Superpixels; Textons