Abstract
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 paper1, 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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Van Leemput, K., Maes, F., Vandermeulen, D., Suetens, P. (2001). A Statistical Framework for Partial Volume Segmentation. In: Niessen, W.J., Viergever, M.A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2001. MICCAI 2001. Lecture Notes in Computer Science, vol 2208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45468-3_25
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DOI: https://doi.org/10.1007/3-540-45468-3_25
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