Segmentation of cerebral MRI scans using a partial volume model, shading correction, and an anatomical prior

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Abstract
A mixture-model clustering algorithm is presented for robust MRI brain image segmentation in the presence of partial volume averaging. The method uses additional classes to represent partial volume voxels of mixed tissue type in the image. Probability distributions for partial volume voxels are modeled accordingly. The image model also allows for tissue-dependent variance values and voxel neighborhood information is taken into account in the clustering formulation. Additionally we extend the image model to account for a low frequency intensity inhomogeneity that may be present in an image. This so-called shading effect is modeled as a linear combination of polynomial basis functions, and is estimated within the clustering algorithm. We also investigate the possibility of using additional anatomical prior information obtained by registering tissue class template images to the image to be segmented. The final result is the estimated fractional amount of each tissue type present within a voxel in addition to the label assigned to the voxel. A parallel implementation of the method is evaluated using synthetic and real MRI data.

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