We introduce a novel approach for identification of objects of interest in volume data. Our approach tries to convey the information contained in two essentially different concepts, the object's boundaries and the narrow solid structures, in an easy and uniform way. The second order derivative operators in directions reaching minimal response are involved for this task. To show the superior performance of our method, we provide a comparison to its main competitor - surface extraction from areas of maximal gradient magnitude. We show that our approach provides the possibility to represent volume data by its subset of a nominal size.
Virtual endoscopy is a promising medical application of volume visualization techniques.
A virtual endoscopy system requires high quality and perspective projection rendering, as well as real-time navigation. This structure represents an approximation of the real organ. The cylindrical structure will be used to accelerate high quality volume rendering. In recent years the Hessian matrix and its eigenvalues became important in pattern recognition. Several algorithms based on the information they provide have been introduced.