Blind separation of tissues in multi-modal MRI using Sparse Component Analysis

Alexander M. Bronstein, Michael M. Bronstein, Michael Zibulevsky, Yehoshua Y. Zeevi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publication13th European Signal Processing Conference, EUSIPCO 2005
Pages1822-1825
Number of pages4
StatePublished - 2005
Externally publishedYes
Event13th European Signal Processing Conference, EUSIPCO 2005 - Antalya, Turkey
Duration: 4 Sep 20058 Sep 2005

Publication series

Name13th European Signal Processing Conference, EUSIPCO 2005

Conference

Conference13th European Signal Processing Conference, EUSIPCO 2005
Country/TerritoryTurkey
CityAntalya
Period4/09/058/09/05

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