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

Soltaninejad, M; Yang, G; Lambrou, T; Allinson, N; Jones, TL; Barrick, TR; Howe, FA; Ye, X

AI Collection

Journal article

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

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

Publication status:: Published

Peer review status:: Peer reviewed

Actions

Email

Email this record

Send the bibliographic details of this record to your email address.

Your Email
Please enter the email address that the record information will be sent to.

-
Your message (optional)
Please add any additional information to be included within the email.
Share
Cite

Cite this record

APA Style

Soltaninejad, M., Yang, G., Lambrou, T., Allinson, N., Jones, T. L., Barrick, T. R., Howe, F. A., & Ye, X. (2016). 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), 183–203.

MLA Style

Soltaninejad, M, et al. “Automated Brain Tumour Detection and Segmentation Using Superpixel-Based Extremely Randomized Trees in FLAIR MRI.” International Journal of Computer Assisted Radiology and Surgery, vol. 12, no. 2, 2016, pp. 183–203.

Chicago Style

Soltaninejad, M, G Yang, T Lambrou, et al. 2016. “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): 183–203.
Print