IEEE Trans Vis Comput Graph. 2019 Sep;25(9):2725-2737. doi: 10.1109/TVCG.2018.2856744. Epub 2018 Jul 17.
We present a volume exploration framework, FeatureLego, that uses a novel voxel clustering approach for efficient selection of semantic features. We partition the input volume into a set of compact super-voxels that represent the finest selection granularity. We then perform an exhaustive clustering of these super-voxels using a graph-based clustering method. Unlike the prevalent brute-force parameter sampling approaches, we propose an efficient algorithm to perform this exhaustive clustering. By computing an exhaustive set of clusters, we aim to capture as many boundaries as possible and ensure that the user has sufficient options for efficiently selecting semantically relevant features. Furthermore, we merge all the computed clusters into a single tree of meta-clusters that can be used for hierarchical exploration. We implement an intuitive user-interface to interactively explore volumes using our clustering approach. Finally, we show the effectiveness of our framework on multiple real-world datasets of different modalities.
我们提出了一个体绘制框架 FeatureLego，它使用一种新颖的体素聚类方法来高效地选择语义特征。我们将输入体数据划分为一组紧凑的超体素，这些超体素代表了最细的选择粒度。然后，我们使用基于图的聚类方法对这些超体素进行详尽的聚类。与流行的暴力参数采样方法不同，我们提出了一种高效的算法来执行这种详尽的聚类。通过计算一组详尽的聚类，我们旨在捕获尽可能多的边界，并确保用户有足够的选项来高效地选择语义相关的特征。此外，我们将所有计算出的聚类合并到一个单一的元聚类树中，该树可用于层次化探索。我们实现了一个直观的用户界面，以便使用我们的聚类方法交互地探索体数据。最后，我们在多个不同模态的真实数据集上展示了我们框架的有效性。
