Segmentation of organs in medical images is a difficult task requiring very often the use of model-based approaches.
To build the model, we need an annotated training set of shape examples with correspondences
indicated among shapes. Manual positioning of landmarks is a tedious, time-consuming, and error prone task,
and almost impossible in the 3D space. To overcome some of these drawbacks, we devised an automatic method
based on the notion of c-scale, a new local scale concept. For each boundary element b, the arc length of the
largest homogeneous curvature region connected to b is estimated as well as the orientation of the tangent at b.
With this shape description method, we can automatically locate mathematical landmarks selected at different
levels of detail. The method avoids the use of landmarks for the generation of the mean shape. The selection of
landmarks on the mean shape is done automatically using the c-scale method. Then, these landmarks are propagated
to each shape in the training set, defining this way the correspondences among the shapes. Altogether
12 strategies are described along these lines. The methods are evaluated on 40 MRI foot data sets, the object of
interest being the talus bone. The results show that, for the same number of landmarks, the proposed methods
are more compact than manual and equally spaced annotations. The approach is applicable to spaces of any
dimensionality, although we have focused in this paper on 2D shapes.