Warping with optimized weighting factors of displacement vectors-a new method to reduce inter-individual variations in brain imaging

An accurate comparison of multimodal and/or inter-individual 3D image datasets of brains requires geometric transformation techniques (warping) to reduce geometric variations. Here, a subset of warping techniques, namely point-based warping, is investigated. For this kind of warping landmarks between datasets have to be defined. In large 3D datasets manual setting of landmarks is time-consuming and therefore impracticable. Consequently we approach this problem by investigating fast automatic procedures for determining landmarks, based on Monte Carlo techniques. The combined methods were tested on 3D autoradiographs of the brains of gerbils. The results are evaluated by three different similarity functions. We found that the combined approach is highly applicable in processing brain images.