An efficient clustering method for fast rendering of time-varying volumetric medical data

Abstract

Visualization and exploration of time-varying volumetric medical data help clinicians for better diagnosis and treatment. However, it is a challenge to render these data in an interactive manner because of their complexity and large size. We propose an efficient clustering method for fast compression and rendering of these large dynamic data. We divide the volumes into a set of blocks and use a BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) based algorithm to cluster them, which can usually find a high quality clustering with a single scan of the blocks. In addition, the granularity of clusters can be adaptively adjusted by dynamically configuring threshold values. In each cluster of blocks, a KeyBlock is generated to represent the cluster, and therefore the storage space of the volumes is reduced greatly. In addition, we assign a lifetime to every KeyBlock and implement a dynamic memory management scheme to further reduce the storage space. During the rendering, each KeyBlock is rendered as a KeyImage, which can be reused if the view transformation and transfer function are not changed. This reuse can help to increase the rendering speed significantly. Experimental results showed that the proposed method can achieve good performance in terms of both speed optimization and space reduction.