Comparison of Different 3D Edge Detection Methods to Define Landmarks for Point-Based Warping in Autoradiographic Brain Imaging

Abstract. Warping can be used to reduce interindividual structural variations of 3D image datasets of brains by generating a standard brain and subsequent matching of individual datasets to this reference system. Point-based warping uses structural information (landmarks) to construct the spatial correspondence between the datasets. For this we compare the performance of three landmark detection algorithms. The first two approaches use a threshold-based definition of landmarks, the third spatial derivations of voxels. The warping is based on a distance-weighted method with an exponential weighting function. All methods tested are able to reduce structural variations, best results are obtained by the derivation approach.