A New Approach to Define Landmarks for Point-Based Warping in Brain Imaging

Abstract

An accurate comparison of inter-individual 3D image datasets of brains requires warping techniques to reduce geometric variations. In this study we use a point-based method of warping with weighted sums of displacement vectors, which is extended by an optimization process. To improve the practicability of 3D warping, we investigate fast automatic procedures for determining landmarks. The combined approach was tested on 3D autoradiographs of brains of Mongolian gerbils. The landmark-generator is based on Monte-Carlo-techniques to detect corresponding reference points at edges of anatomical structures. The warping function is distance-weighted with landmark-specific weighting factors. These weighting factors are optimized by a computational evolution strategy. Within this optimization process the quality of warping is quantified by the sum of spatial differences of manually predefined registration points (registration error). The described approach combines a highly suitable procedure to detect landmarks in brain images and a point-based warping technique, which optimizes local weighting factors. The optimization of the weighting factors improves the similarity between the warped and the target image.