Brain tissue classification of magnetic resonance images using partial volume modeling

This paper presents a fully automatic three-dimensional classification of b rain tissues for Magnetic Resonance (MR) images, An MR image volume may be composed of a mixture of several tissue types due to partial volume effects . Therefore, He consider that in a brain dataset there are not only the thr ee main types of brain tissue: gray matter, white matter, and cerebro spina l fluid, railed pure classes, but also mixtures, called mixclasses. A stati stical model of the mixtures is proposed and studied by means of simulation s. It is shown that it can be approximated by a Gaussian function under som e conditions. The D'Agostino-Pearson normality test is used to assess the r isk alpha of the approximation. In order to classify a brain into three typ es of brain tissue and deal with the problem of partial volume effects, the proposed algorithm uses two steps: 1) segmentation of the brain into pure and mixclasses using the mixture model; 2) reclassification of the mixclass es into the pure classes using knowledge about the obtained pure classes, B oth steps use Markov random held (MRF) models, The multifractal dimension, describing the topology of the brain, is added to the MRFs to improve discr imination of the mixclasses, The algorithm is evaluated using both simulate d images and real MR images with different TI-weighted acquisition sequence s.