Group-wise registration of point sets

First for modes of variations for L2 vertebra.


We present a novel, fast, group-wise registration technique based on establishing soft correspondences between groups of point sets. The registration approach is used to create a statistical shape model, capable of learning the shape variations within a training set. The shape model consists of a mean shape and its transformations to all training shapes. We decouple the procedure into two steps: updating the mean shape and registering it to the training shapes. The algorithm alternates between these two steps until convergence. We perform extensive experiments on two data sets: lumbar spine and hippocampi. We compare our algorithm to available state-of-the-art group-wise registration algorithms including feature-based and volume-based approaches. We demonstrate improved generalization, specificity and compactness compared to these algorithms.


Group-Wise Registration of Point Sets for Statistical Shape Models
Abtin Rasoulian, Robert Rohling, and Purang Abolmaesumi
IEEE Transactions on Medical Imaging, 2012.

Abtin Rasoulian
Last modified: Nov 5, 2012