Fuzzy Markovian segmentation in application of magnetic resonance images
In this paper, we present a fuzzy Markovian method for brain tissue segmentation from magnetic resonance images. Generally, there are three main brain tissues in a brain dataset: gray matter, white matter, and cerebrospinal fluid. However, due to the limited resolution of the acquisition system, many voxels may be composed of multiple tissue types (partial volume effects). The proposed method aims at calculating a fuzzy membership in each voxel to indicate the partial volume degree, which is statistically modeled. Since our method is unsupervised, it first estimates the parameters of the fuzzy Markovian random field model using a stochastic gradient algorithm. The fuzzy Markovian segmentation is then performed automatically. The accuracy of the proposed method is quantitatively assessed on a digital phantom using an absolute average error and qualitatively tested on real MRI brain data. A comparison with the widely used fuzzy C-means algorithm is carried out to show numerous advantages of our method. (C) 2002 Elsevier Science (USA).
brain tissue partial volume effects markovian random fields fuzzy segmentation