TY - GEN
T1 - Blind separation of tissues in multi-modal MRI using Sparse Component Analysis
AU - Bronstein, Alexander M.
AU - Bronstein, Michael M.
AU - Zibulevsky, Michael
AU - Zeevi, Yehoshua Y.
PY - 2005
Y1 - 2005
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84863655570&partnerID=8YFLogxK
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AN - SCOPUS:84863655570
SN - 1604238216
SN - 9781604238211
T3 - 13th European Signal Processing Conference, EUSIPCO 2005
SP - 1822
EP - 1825
BT - 13th European Signal Processing Conference, EUSIPCO 2005
T2 - 13th European Signal Processing Conference, EUSIPCO 2005
Y2 - 4 September 2005 through 8 September 2005
ER -