This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Feb 1997
Volume: 19,
Issue: 2
On page(s): 109-120
ISSN: 0162-8828
References Cited: 31
CODEN: ITPIDJ
INSPEC Accession Number: 5529621
Digital Object Identifier: 10.1109/34.574787
Current Version Published: 2002-08-06
Abstract
A method is presented to segment multidimensional images using a
multiscale (hyperstack) approach with probabilistic linking. A
hyperstack is a voxel-based multiscale data structure whose levels are
constructed by convolving the original image with a Gaussian kernel of
increasing width. Between voxels at adjacent scale levels, child-parent
linkages are established according to a model-directed linkage scheme.
In the resulting tree-like data structure, roots are formed to indicate
the most plausible locations in scale space where segments in the
original image are represented by a single voxel. The final segmentation
is obtained by tracing back the linkages for all roots. The present
paper deals with probabilistic (or multiparent) linking. The multiparent
linkage structure is translated into a list of probabilities that are
indicative of which voxels are partial volume voxels and to which
extent. Probability maps are generated to visualize the progress of weak
linkages in scale space when going from fine to coarser scale. It is
demonstrated that probabilistic linking gives a significantly improved
segmentation as compared with conventional (single-parent) linking
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