Quantitative Stochastic Analysis of Magnetic Resonance Images of the Brain

The aim of our work is to perform an automated tool for brain MRI tissues quantification. The method we develop is based on MRI intensity stochastic analysis. By the use of the Gaussian mixture model for these intensities, we estimate MRI tissues parameters with a combination of the expectation-maximization algorithm and the Markov random field model witch provide contextual constraints that improve the classification of image pixels into three classes of tissue: white matter, grey matter and cerebro-spinal fluid. The automated model based algorithm is also extended to take in account an important MRI artefact: the bias field caused by electromagnetic field inhomogeneities. The resulting automated MRI analysis method simultaneously corrects from MR field inhomogeneities, estimates tissue classes distribution parameters, classifies the image and detects multiple sclerosis lesions when treated images present this pathology. We validate our method on simulated data then on real MRI scans