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In this paper, we demonstrate the interest of the multifractal analysis for removing the ambiguities due to the intensity overlap, and we propose a brain tissue segmentation method from Magnetic Resonance Imaging (MRI) images, which is based on Markov Random Field (MRF) models. The brain tissue segmentation consists in separating the encephalon into the three main brain tissues: grey matter, white matter and cerebrospinal fluid (CSF). The classical MRF model uses the intensity and the neighbourhood information, which is not robust enough to solve problems, such as partial volume effects. Therefore, we propose to use the multifractal analysis, which can provide information on the intensity variations of brain tissues. This knowledge is modelled and then incorporated into a MRF model. This technique has been successfully applied to real MRI images. The contribution of the multifractal analysis is proved by comparison with a classical MRF segmentation using simulated data.

MRI SEGMENTATION USING MULTIFRACTAL ANALYSIS AND MRF MODELS