SORA

Advancing, promoting and sharing knowledge of health through excellence in teaching, clinical practice and research into the prevention and treatment of illness

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

Barrick, TR; Ye, X; Soltaninejad, S; Lambrou, T; Allinson, N; Jones, TL; Howe, FA; Yang, G (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. https://doi.org/10.1007/s11548-016-1483-3
SGUL Authors: Howe, Franklyn Arron

[img]
Preview
PDF Published Version
Available under License Creative Commons Attribution.

Download (7MB) | Preview

Abstract

Purpose 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.

Item Type: Article
Additional Information: 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.
SGUL Research Institute / Research Centre: Academic Structure > Molecular and Clinical Sciences Research Institute (MCS)
Academic Structure > Molecular and Clinical Sciences Research Institute (MCS) > Neuroscience (INCCNS)
Journal or Publication Title: International Journal of Computer Assisted Radiology and Surgery
Dates:
DateEvent
31 August 2016Accepted
20 September 2016Published Online
1 February 2017Published
Publisher License: Creative Commons: Attribution 4.0
Projects:
Project IDFunderFunder ID
C1459/A13303Cancer Research UKhttp://dx.doi.org/10.13039/501100000289
LSHC-CT-2004-503094Seventh Framework Programmehttp://dx.doi.org/10.13039/501100004963
600929Seventh Framework Programmehttp://dx.doi.org/10.13039/501100004963
URI: https://openaccess.sgul.ac.uk/id/eprint/108243
Publisher's version: https://doi.org/10.1007/s11548-016-1483-3

Actions (login required)

Edit Item Edit Item