Point-based registration of images generally depends on the extraction of suitable landmarks. Recently, different 3D operators have been proposed in the literature to detect anatomical point landmarks in 3D images. While the localization performance of 3D operators has already been investigated (e.g., Frantz et al), studies on the detection performance of 3D operators are hardly known. In this paper, we investigate nine 3D differential operators for the detection of 3D point landmarks in MR and CT images. These operators are based on either first, second, or first and second order partial derivatives of an image. In our investigation we use measures, which reflect different aspects of the detection performance of the operators. in the first part of the investigation, we analyze the number of corresponding detections in 3D tomographic images, and in the second part we use statistical measures to determine the detection performance w.r.t. certain landmarks. It turns out that (1) operators based on only first order partial derivative of an image yield a larger number of corresponding points than the other operates and that (2) their performance on the basis of the statistical measures is better.
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