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Author  |
Oualid M. Benkarim; Petia Radeva; Laura Igual | ||||
| 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. |
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| 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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