---
res:
  bibo_abstract:
  - We pose the problem of tissue classification in MRI as a blind source separation
    (BSS) problem and solve it by means of sparse component analysis (SCA). Assuming
    that most MR images can be sparsely represented, we consider their optimal sparse
    representation. Sparse components define a physically-meaningful feature space
    for classification. We demonstrate our approach on simulated and real multi-contrast
    MRI data. The proposed framework is general in that it is applicable to other
    modalities of medical imaging as well, whenever the linear mixing model is applicable.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Alexander
      foaf_name: Bronstein, Alexander
      foaf_surname: Bronstein
      foaf_workInfoHomepage: http://www.librecat.org/personId=58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
    orcid: 0000-0001-9699-8730
  - foaf_Person:
      foaf_givenName: M.M.
      foaf_name: Bronstein, M.M.
      foaf_surname: Bronstein
  - foaf_Person:
      foaf_givenName: M.
      foaf_name: Zibulevsky, M.
      foaf_surname: Zibulevsky
  - foaf_Person:
      foaf_givenName: Y.Y.
      foaf_name: Zeevi, Y.Y.
      foaf_surname: Zeevi
  bibo_doi: 10.1109/icip.2005.1530297
  bibo_volume: 2
  dct_date: 2005^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/0780391349
  dct_language: eng
  dct_publisher: IEEE@
  dct_title: '"Unmixing" tissues: Sparse component analysis in multi-contrast MRI@'
...