Abstract:
We present a novel approach, clustering on local image
profiles, for statistically characterizing image intensity in
object boundary regions. In deformable model
segmentation, a driving consideration is the geometry to
image match, the degree to which the target image
conforms to some template within the object boundary
regions. The template should account for variation over a
training set and yet be specific enough to drive an
optimization to a desirable result. Using clustering, a
template can be built that is optimal over the training data
in the metric used, such as normalized correlation. We
present a method that first determines local crossboundary
image profile types in the space of training data
and then builds a template of optimal types.
Also presented are the results of a study using this
approach on the human kidney in the context of medial
representation deformable model segmentation. The
results show an improvement in the automatic
segmentations using the cluster template, over a
previously built template.
The pdf of the paper.
The poster I presented.
Slides I presented of this research at the 2004 UNC Radiology Research Symposium.
with relevant avi's ani_1.avi, ani_2.avi,
ani_4.avi, ani_5.avi.