Towards an image quality index in tomographic volume visualization

摘要

While the importance of proper segmentation or registration for the results of volume visualization is well-known, the influence of the actual visualization steps has found only little attention so far. From a signal processing point of view, a typical volume visualization pipeline is mostly a sequence of sampling and convolution operations. In this paper, we are interested how image quality in terms of blurring and aliasing artifacts depends on the choice of methods and parameters for these steps. For this purpose, signal transfer from tomographic image acquisition to ray casting visualization is analyzed in frequency space. Deterioration of a test signal along the steps of the pipeline is measured in terms of remaining useful energy and introduced aliasing energy. Effects of changing variables, such as slice thickness, interpolation method, or sampling density on the ray, are experimentally investigated. Quantitative results are related to visible effects, using simulated test data. The presented method may be used to optimize the volume visualization pipeline. Vice versa, for a 3D image with known processing parameters, it may be estimated whether structures of interest will be visible, smoothed out, or hidden among aliasing artifacts.