Tissue Classification from Brain Perfusion MR Images Using Expectation-Maximization Algorithm Initialized by Hierarchical Clustering on Whitened Data

Abstract

Classification of different perfusion compartments in the brain is important to the profound analysis of brain perfusion. We presents a method based on a mixture of multivariate Gaussians (MoMG) and the expectation-maximization (EM) algorithm initialized by the results of hierarchical clustering (HC) on the whitened data to automatically dissect various perfusion compartments from dynamic susceptibility contrast (DSC) MR images so that each compartment comprises pixels of similar signal-time curves. Monte Carlo simulations have been designed and executed to assess the performance of the proposed method under various signal-to-noise ratios (SNRs). The results of simulations showed that using EM initialized by HC on whitened data produce the best accuracy of segmentation. Five healthy volunteers participated in this study for the validation of this method. The averaged ratios of gray matter to white matter for relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF) and mean transition time (MTT) derived from 5 normal subjects were 2.196±0.097, 2.259±0.119, and 0.968±0.023 which are in good agreement with those reported in the literature. The proposed method can subserve the diagnosis and assessment of various diseases involving the changes of cerebral blood distribution.