Optimized Conformal Surface Registration with Shape-based Landmark Matching

Surface registration, which transforms different sets of surface data into one common reference space, is an important process which allows people to compare or integrate the surface data effectively. In this work, the authors are interested in looking for meaningful registrations between surfaces through parameterizations, using prior features in the form of landmark curves on the surfaces. In particular, they generate optimized conformal parameterizations which match landmark curves exactly with shape-based correspondences between them. They propose a variational method to minimize a compound energy functional that measures the harmonic energy of the parameterization maps and the shape dissimilarity between mapped points on the landmark curves. By using the local surface geometry on the curves to define a shape measure, they compute registrations that ensure consistent correspondences between anatomical features. They test their algorithm on synthetic surface data. An application of their model to medical imaging research is shown, using experiments on brain cortical surfaces, with anatomical (sulcal) landmarks delineated. This ensures correct averaging and comparison of data across subjects.