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Anatomical landmarks, i.e. well defined points in
the anatomy provide meaningful information on the
local geometry. They are widely used to analyze
shapes or as control points for many registration
algorithms. However there detection, which remains
manual, is a tedious and time-consuming task, even
for specialists.
We
propose a generic algorithm to detect landmarks in a
new image. Using a training set of hand-labeled
images, we learn the local geometry. In a new image,
the location of the landmark(s) is given by
likelihood maximization.
Examples of landmarks:
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HoH : apex of the Head of the Hippocampus (1)
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HT
: tail of the Hippocampus (2)
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UA
: posterior Apex of the hippocampal Uncus (3)
More
details in this poster or
the publications. |
