title: Within-brain classification for brain tumor segmentation.
abstract: OBJECTIVE
  In this paper, we investigate a framework for interactive brain tumor segmentation which, at its core, treats the problem of interactive brain tumor segmentation as a machine learning problem.
  
  
  METHODS
  This method has an advantage over typical machine learning methods for this task where generalization is made across brains. The problem with these methods is that they need to deal with intensity bias correction and other MRI-specific noise. In this paper, we avoid these issues by approaching the problem as one of within brain generalization. Specifically, we propose a semi-automatic method that segments a brain tumor by training and generalizing within that brain only, based on some minimum user interaction.
  
  
  CONCLUSIONS
  We investigate how adding spatial feature coordinates (i.e., i, j, k) to the intensity features can significantly improve the performance of different classification methods such as SVM, kNN and random forests. This would only be possible within an interactive framework. We also investigate the use of a more appropriate kernel and the adaptation of hyper-parameters specifically for each brain.
  
  
  RESULTS
  As a result of these experiments, we obtain an interactive method whose results reported on the MICCAI-BRATS 2013 dataset are the second most accurate compared to published methods, while using significantly less memory and processing power than most state-of-the-art methods.
authors.affiliation: Université de Sherbrooke, 2500 Boul. de l'Université, Sherbrooke, QC, J1K 2R1, Canada. mohammad.havaei@gmail.com.
authors.forename: Mohammad
authors.initials: M
authors.lastname: Havaei
authors.affiliation: Université de Sherbrooke, 2500 Boul. de l'Université, Sherbrooke, QC, J1K 2R1, Canada.
authors.forename: Hugo
authors.initials: H
authors.lastname: Larochelle
authors.affiliation: Université de Sherbrooke, 2500 Boul. de l'Université, Sherbrooke, QC, J1K 2R1, Canada.
authors.forename: Philippe
authors.initials: P
authors.lastname: Poulin
authors.affiliation: Université de Sherbrooke, 2500 Boul. de l'Université, Sherbrooke, QC, J1K 2R1, Canada.
authors.forename: Pierre-Marc
authors.initials: PM
authors.lastname: Jodoin
mesh_terms: D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging*; D001932:Brain Neoplasms / Q000000981:diagnostic imaging*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods
keywords: Brain tumor segmentation; Computer-aided detection; Interactive; Machine learning; Segmentation; Within-brain generalization