Spherical Demons Registration of Spherical Surfaces

As demonstrated by the example videos accompanying this submission of the multiresolution implementation of Spherical Demons, the registration appears reasonable. However, we are still unable to replicate the warps from the stable and well verified implementation of this algorithm url{http://sites.google.com/site/yeoyeo02/software/sphericaldemonsrelease}. We find the average warp discrepancies between the original implementation and ITK implementation to be about 5.3 mm on a sphere of radius 100.0 mm (note that we do not expect a 100% agreement because of implementation differences, but 5.3 mm is relatively large). The submission also includes cortical surface meshes of 39 subjects and the corresponding segmentation labels of the cortical surfaces. Ultimately, the best validation would be to compare the overlap of these segmentation labels after registration. We welcome fellow ITK developers to work on this. Please note that this ITK implementation of the algorithm is currently being reviewed and tested in the NAMIC Sandbox at: url{http://svn.na-mic.org/NAMICSandBox/trunk/QuadEdgeMeshSphericalDiffeomorphicDemonsRegistration/}. If you want access to the stable and well verified implementation of this algorithm, please use the MATLAB code available at: url{http://sites.google.com/site/yeoyeo02/software/sphericaldemonsrelease}. This document describes a contribution to the Insight Toolkit intended to support the process of performing deformable registration on two Meshes. The method implemented here is restricted to Meshes with a Spherical geometry and topology, and with scalar values associated to their nodes. The code described here is an implementation of the paper “Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration” by Yeo, Sabuncu, Vercauteren, Ayache, Fischl and Golland [3, 4]. This paper is accompanied with the source code, input data, parameters and output data that we used for validating the algorithm described in this paper. This adheres to the fundamental principle that scientific publications must facilitate reproducibility of the reported results.