  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.
