Summary
Objectives:
To point out the problem of non-uniform landmark placement in statistical shape modeling,
to present an improved method for generating landmarks in the 3D case and to propose
an unbiased evaluation metric to determine model quality.
Methods:
Our approach minimizes a cost function based on the minimum description length (MDL)
of the shape model to optimize landmark correspondences over the training set. In
addition to the standard technique, we employ an extended remeshing method to change
the landmark distribution without losing correspondences, thus ensuring a uniform
distribution over all training samples. To breakthe dependency of the established
evaluation measures generalization and specificity from the landmark distribution,
we change the internal metric from landmark distance to volumetric overlap.
Results:
Redistributing landmarks to an equally spaced distribution during the model construction
phase improves the quality of the resulting models significantly if the shapes feature
prominent bulges or other complex geometry.
Conclusions:
The distribution of landmarks on the training shapes is – beyond the correspondence
issue – a crucial point in model construction.
Keywords
Image interpretation - computer-assisted statistical models
