Evaluation of 3D Operators for the Detection of Anatomical Point Landmarks in MR and CT Images

Point-based registration of images strongly depends on the extraction of suitable landmarks. Recently, different 3D operators have been proposed in the literature for the detection of anatomical point landmarks in 3D images. In this paper, we investigate nine 3D differential operators for the detection of point landmarks in 3D 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 under elastic deformations and noise, in the second part, we use statistical measures to determine the detection performance for landmarks within regions of interest (ROIs), and in the third part, we investigate the separability of the detections. It turns out that operators based on only first order partial derivatives of an image (i) yield a larger number of corresponding points than the other operators, (ii) that their performance on the basis of the statistical measures is better, and (iii) that the separability of the detections is better so that a suitably chosen threshold can significantly decrease the number of false detections.