@InProceedings{OualidM.Benkarim2014,
author="Oualid M. Benkarim
and Petia Radeva
and Laura Igual",
title="Label Consistent Multiclass Discriminative Dictionary Learning for MRI Segmentation",
booktitle="8th Conference on Articulated Motion and Deformable Objects",
year="2014",
publisher="Springer International Publishing",
volume="8563",
pages="138--147",
optkeywords="MRI segmentation",
optkeywords="sparse representation",
optkeywords="discriminative dic- tionary learning",
optkeywords="multiclass classication",
abstract="The automatic segmentation of multiple subcortical structures in brain Magnetic Resonance Images (MRI) still remains a challenging task. In this paper, we address this problem using sparse representation and discriminative dictionary learning, which have shown promising results in compression, image denoising and recently in MRI segmentation. Particularly, we use multiclass dictionaries learned from a set of brain atlases to simultaneously segment multiple subcortical structures.We also impose dictionary atoms to be specialized in one given class using label consistent K-SVD, which can alleviate the bias produced by unbalanced libraries, present when dealing with small structures. The proposed method is compared with other state of the art approaches for the segmentation of the Basal Ganglia of 35 subjects of a public dataset.The promising results of the segmentation method show the eciency of the multiclass discriminative dictionary learning algorithms in MRI segmentation problems.",
optnote="MILAB; OR",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2494), last updated on Thu, 18 Sep 2014 10:18:31 +0200",
isbn="978-3-319-08848-8",
issn="0302-9743",
doi="10.1007/978-3-319-08849-5_14",
file=":http://refbase.cvc.uab.es/files/BRI2014.pdf:PDF"
}