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Title: A unifying framework for partial volume segmentation of brain MR images
Authors: Van Leemput, Koen ×
Maes, Frederik
Vandermeulen, Dirk
Suetens, Paul #
Issue Date: Jan-2003
Publisher: Institute of Electrical and Electronics Engineers
Series Title: IEEE transactions on medical imaging vol:22 issue:1 pages:105-119
Abstract: Accurate brain tissue segmentation by intensity-based voxel classification of magnetic resonance (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 encompasses and extends existing techniques. We start from a commonly used parametric statistical image model in which each voxel belongs to one single tissue type, and introduce an additional downsampling step that causes partial voluming along the borders between tissues. An expectation-maximization approach is used to simultaneously estimate the parameters of the resulting model and perform a PV classification. We present results on well-chosen simulated images and on real MR images of the brain, and demonstrate that the use of appropriate spatial prior knowledge not only improves the classifications, but is often indispensable for robust parameter estimation as well. We conclude that general robust PV segmentation of MR brain images requires statistical models that describe the spatial distribution of brain tissues more accurately than currently available models.
Description: Van Leemput K., Maes F., Vandermeulen D., Suetens P., ''A unifying framework for partial volume segmentation of brain MR images'', IEEE transactions on medical imaging, vol. 22, no. 1, pp. 105-119, January 2003.
ISSN: 0278-0062
Publication status: published
KU Leuven publication type: IT
Appears in Collections:ESAT - PSI, Processing Speech and Images
× corresponding author
# (joint) last author

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