3D Parametric Intensity Models for the Localization of Different Types of 3D Anatomical Point Landmarks in Tomographic Images

Abstract

Abstract. We introduce a new approach for the localization of 3D anatomical point landmarks based on 3D parametric intensity models which are directly fit to an image. We propose different analytic intensity models based on the Gaussian error function in conjunction with 3D rigid transformations as well as deformations to efficiently model tip-like, saddle-like, and sphere-like structures. The approach has been successfully applied to accurately localize anatomical landmarks in 3D MR and 3D CT image data. We have also compared the experimental results with the results of a previously proposed 3D differential operator. It turns out that the new approach significantly improves the localization accuracy.

Automatic Localization of Anatomical Point Landmarks for Brain Image Processing Algorithms

“…The computation of partial derivatives makes this approach relatively sensitive to image noise and small intensity variations. Deformable and parametric intensity models have been used to detect tip-and saddle-like structures in images of the head (Frantz et al 2000;Alker et al 2001;Wörz et al 2003) by fitting model parameters to the image data through optimization of edge-based measures. But these approaches have not been extended to other brain structure types.…”