Curvature- and Model-Based Surface Hatching of Anatomical Structures Derived from Clinical Volume Datasets

Abstract. We present a texture-based method to hatch anatomical structures derived from clinical volume datasets. We consider intervention planning, where object and shape recognition are supported by adding hatching lines to the anatomical model. The major contribution of this paper is to enhance the curvature-based hatching of anatomical surfaces by incorporating model-based preferential directions of the underlying anatomical structures. For this purpose, we apply our method on vessels and elongated muscles. Additionally, the whole hatching process is performed without time-consuming user interaction for defining stroke direction and surface parameterization. Moreover, our approach fulfills requirements for interactive explorations like frame coherence and real time capability.