title: Brain Tumour Segmentation based on Extremely Randomized Forest with high-level features.
abstract: Gliomas are among the most common and aggressive brain tumours. Segmentation of these tumours is important for surgery and treatment planning, but also for follow-up evaluations. However, it is a difficult task, given that its size and locations are variable, and the delineation of all tumour tissue is not trivial, even with all the different modalities of the Magnetic Resonance Imaging (MRI). We propose a discriminative and fully automatic method for the segmentation of gliomas, using appearance- and context-based features to feed an Extremely Randomized Forest (Extra-Trees). Some of these features are computed over a non-linear transformation of the image. The proposed method was evaluated using the publicly available Challenge database from BraTS 2013, having obtained a Dice score of 0.83, 0.78 and 0.73 for the complete tumour, and the core and the enhanced regions, respectively. Our results are competitive, when compared against other results reported using the same database.
authors.forename: Adriano
authors.initials: A
authors.lastname: Pinto
authors.forename: Sergio
authors.initials: S
authors.lastname: Pereira
authors.forename: Higino
authors.initials: H
authors.lastname: Correia
authors.forename: J
authors.initials: J
authors.lastname: Oliveira
authors.forename: Deolinda M L D
authors.initials: DM
authors.lastname: Rasteiro
authors.forename: Carlos A
authors.initials: CA
authors.lastname: Silva
mesh_terms: D000465:Algorithms; D001932:Brain Neoplasms; D005910:Glioma; D006801:Humans; D008279:Magnetic Resonance Imaging; D012984:Software