@inproceedings{a024ef5c186742d4857628e87fe30095,
title = "A supervised approach for multiple sclerosis lesion segmentation using context features and an outlier map",
abstract = "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 volumes 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.",
author = "Mariano Cabezas and Arnau Oliver and Jordi Freixenet and Xavier Llad{\'o}",
year = "2013",
doi = "10.1007/978-3-642-38628-2_93",
language = "English",
isbn = "9783642386275",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer, Springer Nature",
pages = "782--789",
editor = "Sanches, {Jo{\~a}o M.} and Luisa Mic{\'o} and Cardoso, {Jaime S.}",
booktitle = "Pattern recognition and image analysis",
address = "United States",
note = "Iberian Conference on Pattern Recognition and Image Analysis (6th : 2013), IbPRIA 2013 ; Conference date: 05-06-2013 Through 07-06-2013",
}