W.J. Niessen, K.L. Vincken, J. Weickert, B.M. ter Haar Romeny,
M.A. Viergever,
Multiscale segmentation of three-dimensional MR brain images,
International Journal of Computer Vision, Vol. 31, 185-202, 1999.
Segmentation of MR brain images using intensity values is severely
limited owing to field inhomogeneities, suscetibility artifacts and
partial volume effects. Edge based segmentation methods suffer from
spurious edges and gaps in boundaries. A multiscale method to MRI brain
segmentation is presented which uses both edge and intensity information.
First a multiscale representation of an image is created, which can be
made edge dependent to favor intra-tissue diffusion over inter-tissue
diffusion. Subsequently a multiscale linking model (the hyperstack) is
used to group voxels into a number of objects based on intensity. It is shown
that both an improvement in accuracy and a reduction in image post-processing
can be achieved if edge dependent diffusion is used instead of linear
diffusion. The combination of edge dependent diffusion and intensity
based linking facilitates segmentation of grey matter, white matter and
cerebrospinal fluid with minimal user interaction. To segment the total
brain (white matter plus grey matter) morphological operations are
applied to remove small bridges between the brain and the cranium.
If the total brain is segmented, grey matter, white matter and
cerebrospinal fluid can be segmented by joining a small number of
segments. Using a supervised segmentation technique and MRI simulations
of a brain phantom for validation it is shown that the errors are in
the order or smaller than reported in the literature.
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