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Conclusions
The goal of this environment is to move the expert medical knowledge of
the end user into the visualization and segmentation process. This is accomplished
by merging the activities of visualization and segmentation into a intuitive
action completely under the user's control. In this way, segmentation choices
are made in parallel with the visualization of the data set allowing the
user to control the process based upon context knowledge and what they
expect to see. Furthermore, the process allows the user to interactively
concentrate computational resources on imaging those features of greatest
value.
It also presents a possibility for bridging the gap between the level
of structure describable in the language of image processing and the higher
level features of interest to users. The structural and contextual knowledge
contained in knowledge bases of the kind described in [1]
could be used to interactively support the user during visualization and
segmentation.
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