Automatic multiple sclerosis (MS) lesion segmentation in magnetic resonance imaging (MRI) is a challenging task due to the small size of the lesions, its heterogeneous shape and distribution, overlapping tissue intensity distributions, and the inherent artifacts of MRI. In this paper we propose a pipeline for MS lesion segmentation that combines prior knowledge and contextual information into a boosting classifier. The prior knowledge is introduced in terms of atlas distribution of the main brain tissues while the contextual information is based on a large set of features describing the spatial context in the lesion neighbourhood. Besides, we investigate the inclusion of a probability map describing the likelihood of a voxel to be an outlier, i.e. not being part of any healthy tissue. The experimental results, performed using a set of 30 MRI vol- umes of MS patients with very different lesion load, shows the feasibility of our approach. Besides, the results demonstrate the benefits of taking the outlier map into account.
['Mariano Cabezas', 'Arnau Oliver', 'Jordi Freixenet', 'Xavier Lladó']
A Supervised Approach for Multiple Sclerosis Lesion Segmentation Using Context Features and an Outlier Map