On 3D differential operators for detecting point landmarks

Point-based registration of volume image data heavily relies on the selection of suitable landmarks. In this contribution, we study the extraction of 3D point landmarks from images. Point landmarks have a unique position which can be deduced from the intensity variations in a sufficiently large neighborhood around the prominent point. We propose four 3D differential operators which are generalizations of existing 2D operators for detecting points of high intensity variations. In comparison to previous approaches, our operators have the advantage that only low order partial derivatives of the image function are necessary. Therefore, these operators are computationally efficient and do not suffer from instabilities of computing high order partial derivatives. We also describe how the 3D operators can be generalized to be used on images of arbitrary dimension. First experimental results will be presented on medical imagery.