. In this paper a new gradient estimation method is presented which is based on linear regression. Previous contextual shading techniques try to fit an approximate function to a set of surface points in the neighborhood of a given voxel. Therefore, a system of linear equations has to be solved using the computationally expensive Gaussian elimination. In contrast, our method approximates the density function itself in a local neighborhood with a 3D regression hyperplane. This approach also leads to a system of linear equations but we will show that it can be solved with an efficient convolution. Our method provides at each voxel location the normal vector and the translation of the regression hyperplane which are considered as a gradient and a filtered density value respectively. Therefore, this technique can be used for surface smoothing and gradient estimation at the same time. 1 Introduction In direct volume rendering the quality of the generated image is strongly influ...

This paper is an attempt to review the state-of-the-art cortical segmentation techniques in 2-D and 3-D using brain magnetic resonance imaging (MRI), their applications and new challenges

In biomedical computing the need for visualization methods of the human vascular system has been triggered by recent advances in image acquisition technology. In this paper we describe a number of approaches for the threedimensional display of vessels from volumetric datasets. Our approaches are based on the analysis of the deficiencies of the Maximum Intensity Projection algorithm which today is the state-of-the-art technique for vascular display, and take into account different diagnostic and therapeutic situations. For the qualitative as well as quantitative evaluation of the major vessels, e.g. the carotids, a model-driven Computer Vision method to segment, reconstruct and render the vascular tree surface including branches is presented. For the assessment of smaller vessels a Pattern Recognition technique for contrast enhancement of line-like structures is introduced. It serves as a preprocessing step prior to the application of a volume-rendering algorithm which consi...

In this paper we focus on one of the key problems of Scientific Visualization, the object recognition dilemma. The necessity to pre-interpret application data in order to classify object surface voxels prior to rendering has prevented many visualization methods from becoming practical. We propose the concept of vision by visualization which integrates Computer Vision methods into the visualization process. Based on this, we present the vision camera, a new tool allowing for interactive object recognition during volume data walkthroughs. This camera model is characterized by a flexible front-plane which, under the control of user-specified parameters and image features elastically matches to object surfaces, while shifted through a data volume. Thus, objects are interactively carved out and can be visualized by standard volume visualization methods. Implementation and application of the model are described. Our results suggest that by the integration of human and machine vision new pers...