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A multivariate hypothesis testing framework for tissue clustering and classification of DTI data
Author(s) -
Freidlin Raisa Z.,
Özarslan Evren,
Assaf Yaniv,
Komlosh Michal E.,
Basser Peter J.
Publication year - 2009
Publication title -
nmr in biomedicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.278
H-Index - 114
eISSN - 1099-1492
pISSN - 0952-3480
DOI - 10.1002/nbm.1383
Subject(s) - diffusion mri , cluster analysis , voxel , pattern recognition (psychology) , computer science , artificial intelligence , homogeneity (statistics) , hierarchical clustering , multivariate statistics , fractional anisotropy , statistical hypothesis testing , data mining , mathematics , statistics , machine learning , magnetic resonance imaging , medicine , radiology
Abstract The primary aim of this work is to propose and investigate the effectiveness of a novel unsupervised tissue clustering and classification algorithm for diffusion tensor MRI (DTI) data. The proposed algorithm utilizes information about the degree of homogeneity of the distribution of diffusion tensors within voxels. We adapt frameworks proposed by Hext and Snedecor, where the null hypothesis of diffusion tensors belonging to the same distribution is assessed by an F ‐test. Tissue type is classified according to one of the four possible diffusion models, the assignment of which is determined by a parsimonious model selection framework based on Schwarz Criterion. Both numerical phantoms and diffusion‐weighted imaging (DWI) data obtained from excised rat and pig spinal cords are used to test and validate these tissue clustering and classification approaches. The unsupervised clustering method effectively identifies distinct regions of interest (ROIs) in phantoms and real experimental DTI data. Copyright © 2009 John Wiley & Sons, Ltd.