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Author (up) Oualid M. Benkarim; Petia Radeva; Laura Igual edit   pdf
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  Title Label Consistent Multiclass Discriminative Dictionary Learning for MRI Segmentation Type Conference Article
  Year 2014 Publication 8th Conference on Articulated Motion and Deformable Objects Abbreviated Journal  
  Volume 8563 Issue Pages 138-147  
  Keywords MRI segmentation; sparse representation; discriminative dic- tionary learning; 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.
 
  Address Palma de Mallorca; July 2014  
  Corporate Author Thesis  
  Publisher Springer International Publishing Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN 0302-9743 ISBN 978-3-319-08848-8 Medium  
  Area Expedition Conference AMDO  
  Notes MILAB; OR Approved no  
  Call Number Admin @ si @ BRI2014 Serial 2494  
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