A Statistical Framework for Partial Volume Segmentation

Accurate brain tissue segmentation by intensity-based voxel classification of MR images is complicated by partial volume (PV) voxels that contain a mixture of two or more tissue types. In this paper, we present a statistical framework for PV segmentation that combines and extends existing techniques. We think of a partial volumed image as a downsampled version of a fictive higher-resolution image that does not contain partial voluming, and we estimate the model parameters of this underlying image using an Expectation-Maximization algorithm. This leads to an iterative approach that interleaves a statistical classification of the image voxels using spatial information and an according update of the model parameters. We illustrate the performance of the method on simulated data and on 2-D slices of real MR images. We demonstrate that the use of appropriate spatial models not only improves the classification, but is often indispensable for robust parameter estimation as well.

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