Multi-instance rendering based on dynamic differential surface propagation

High-quality rendering of a complex system usually depends on accurate segmentation of the corresponding objects. In medical imaging data, it is difficult to extract and visualize multiple related objects simultaneously due to the noise, shape variance, and resolution difference of the objects. In this paper we present a viable method to adaptively extract the isosurfaces of different yet informatively related tissues simultaneously from the multimodality imaging data of the brain based on the statistical Partial Differential Equation (PDE) deformable models. Our system and experiments demonstrate the power of using explicit PDE models in extracting and rendering of multiple objects.