Abstract

The literature about partial volume (PV) segmentation of MR images is rather limited, and a general methodology for robustly classifying images with severe partial voluming that works well in all cases, remains an open issue. In this paper, we present a statistical framework for PV segmentation that contains 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 demonstrate on simulated data that the use of appropriate spatial prior knowledge, in casu a Markov random field model, not only improves the classifications, but is often indispensable for robust parameter estimation as well. We also present results on 2-D slices of real high-resolution MR images of the brain, and conclude that general robust segmentation of lower-resolution images requires development of spatial models that accurately describe the shape of the brain.

Citations