Fast retrieval using complete or partial shapes of
organs is an important functionality in medical image databases.
Shapes of organs can be defined as points in shape spaces, which,
in turn, are curved manifolds with a well-defined metric. In this
paper, we experimentally compare two indexing techniques for shape
spaces: first, we re-embed the shape space in a Euclidean space
and use co-ordinate based indexing, and second, we used metric
based hierarchical clustering for directly indexing shape space.
The relative performances are evaluated with images from the
NHANES II database of lumbar and cervical spine x-ray images on a
shape similarity query. The experiments show that indexing using
re-embedding is superior to cluster-based indexing.