Reducing False Detections in Extracting 3D Anatomical Point Landmarks

. Applying 3D di#erential operators to extract point landmarks from medical images generally su#ers from false detections. A considerable number of these false detections is caused by neighboring structures that are included in the region-of-interest #ROI# speci#ed by the observer. The main contributions of this paper are two di#erent approaches to reducing false detections resulting from neighboring structures. First, we present a statistical di#erential approach to selecting a suitable ROI size automatically. Second, we propose a di#erential approach to incorporating prior knowledge of the intensity structure at a landmark. Also, to cope with anisotropic voxel sizes in estimating partial derivatives, we implemented a computationally e#cientscheme based on cubic B-spline image interpolation. Experimental results based on 3D MR and CT images of the human head are presented. Keywords: Point landmarks, di#erential operators, false detections 1 Introduction Anatomical landm..

Reducing False Detections in Extracting 3D Anatomical Point Landmarks

Applying 3D differential operators to extract point landmarks from medical images generally suffers from false detections. A considerable number of these false detections is caused by neighboring structures that are included in the region-of-interest (ROI) specified by the observer. The main contributions of this paper are two different approaches to reducing false detections resulting from neighboring structures. First, we present a statistical differential approach to selecting a suitable ROI size automatically. Second, we propose a differential approach to incorporating prior knowledge of the intensity structure at a landmark. Also, to cope with anisotropic voxel sizes in estimating partial derivatives, we implemented a computationally efficient scheme based on cubic B-spline image interpolation. Experimental results based on 3D MR and CT images of the human head are presented