  Purpose: 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.
  Conclusion: 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.
