Optimization of Landmark Selection for Cortical Surface Registration

Manually labeled landmark sets are often required as in- puts for landmark-based image registration. Identifying an optimal subset of landmarks from a training dataset may be useful in reducing the labor intensive task of manual label- ing. In this paper, we present a new problem and a method to solve it: given a set of N landmarks, find the k(< N) best landmarks such that aligning these k landmarks that produce the best overall alignment of all N landmarks. The resulting procedure allows us to select a reduced number of landmarks to be labeled as a part of the registration proce- dure. We apply this methodology to the problem of register- ing cerebral cortical surfaces extracted from MRI data. We use manually traced sulcal curves as landmarks in perform- ing inter-subject registration of these surfaces. To minimize the error metric, we analyze the correlation structure of the sulcal errors in the landmark points by modeling them as a multivariate Gaussian process. S...

• Type: Conference
• Year: 2009
• Where: CVPR
• Authors: Anand A. Joshi, David W. Shattuck, Dimitrios Pantazis, Hanna Damasio, Quanzheng Li, Richard M. Leahy
• Keywords: Computer Vision, Cortical Surface Registration, CVPR 2009, Landmark Selection