Knowledge-
Guided Segmentation
of 3D Imagery
Rakesh Mullick
Norberto F. Ezquerra
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Abstract
This paper presents a computationally efficient and
robust approach to locate, label and isolate
three-dimensional (3D) structures from discrete 3D
imagery. The emphasis is placed on extracting a convex,
singly connected 3D structure of interest imbedded in
discrete, volumetric data sets that are sparse, noisy,
and possibly misleading. In particular, we focus on
the segmentation of man-made objects (phantoms) imaged
tomographically and on the segmentation of the
myocardial mass obtained in 3D nuclear cardiac imagery.
The salient characteristics of this method are that the
segmentation process is accomplished in a fully
automated fashion and that the volume of interest can
be isolated and labeled even in cases where ambiguities
or structural incompleteness are inherent in the
original imagery. The method presented here can be
viewed as a knowledge-guided approach that is
iterative and self-correcting, and that is shown to
consistently evolve toward incrementally refined
segmentation solutions that are quantitatively and
qualitatively accurate. The approach innovatively
combines image analysis techniques with morphological,
knowledge-based, and model-based grouping operations
in a highly integrated fashion. In the subsequent
discussions, we fully describe the underlying
mathematical and algorithmic details of the approach,
and discuss the results obtained from its application
in numerous experimental studies.
Graphical Models and Image Processing
Vol. 58, No. 6, November, pp510-523, 1996
Article No. 0043
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