Shape modeling via local curvature scale

Segmentation and modeling of organs using model-based approaches require a priori information which is often given by manually tagging landmarks on a training set of shapes. This is a tedious, time-consuming, and error prone task. To overcome some of these drawbacks, focusing on 2D shapes, we devised an automatic method based on the notion of curvature scale – a new local scale concept. This shape descriptor is used to automatically locate mathematical landmarks on the mean of the shapes in the training set, which are then propagated to the training shapes. Altogether 12 different strategies are described and are evaluated in different combinations in terms of compactness on two data sets – 40 CT images of the liver and 40 MR images of the talus bone of the foot. The results show that, for the same number of landmarks, the proposed methods are more compact than manual and equally spaced annotations.