: This paper describes a method for the enhancement of curvilinear structures such as vessels and bronchi in 3D medical images. A 3D line enhancement filter is developed with the aim of discriminating line structures from other structures and recovering line structures of various widths. The 3D line filter is based on a combination of the eigenvalues of the 3D Hessian matrix. Multi-scale integration is formulated by taking the maximum among single-scale filter responses, and its characteristics are examined to derive criteria for the selection of parameters in the formulation. The resultant multi-scale line-filtered images provide significantly improved segmentation and visualization of curvilinear structures. The usefulness of the method is demonstrated by the segmentation and visualization of brain vessels from MRI (magnetic resonance imaging) and MRA (magnetic resonance angiography), bronchi from a chest CT, and liver vessels (portal veins) from an abdominal CT. Keywords: 3D image ...

This paper describes 3D image filters for the enhancement of specific local intensity structures such as line and sheet, and its application to tissue classification for volume rendering. Multi-channel classification is performed by combining different 3D image filter outputs. The resulted method significantly enlarges the scope of volume rendering, especially in the medical domain. We show the usefulness of the method for different visualization problems. 1 Introduction Volume rendering is a powerful visualization tool especially for medical application [1],[2],[3],[4]. Basic requirement in medical application is to visualize specific tissues of interest with the relation to surrounding structures. Tissue classification is one of the most important processes in the volume rendering pipeline. Most commonly, this process is done based on the histogram of intensity values in original 3D images. Probabilistic or fuzzy classification has been used instead of binary classification in orde...