Visualization of vasculature from volume data

Abstract 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 three-dimensional 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 takes 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 that consists of an advanced maximum projection scheme with depth-cueing. The integrated 3D display of soft-tissue surfaces with adjacent vasculature, as required, e.g., for neurosurgery planning, is enabled by a raycaster, simultaneously rendering and merging two volume datasets. We emphasize the clinical relevance of techniques for explorative volume data analysis by a walkthrough example. The paper demonstrates the necessity of incorporating Computer Vision and Pattern Recognition methodologies into the Scientific Visualization pipeline for biomedical imaging.