A multivariate hypothesis testing framework for tissue clustering and classification of DTI data.

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.