  In many applications of X-ray computed tomography, an unsupervised
segmentation of the reconstructed 3D volumes forms an important step in the
image processing chain for further investigation of the digitized object.
Therefore, the goal is to train a clustering algorithm on the volume, which
produces a voxelwise classification by assigning a cluster index to each voxel.
However, clustering methods, e.g., K-Means, typically have an asymptotic
polynomial runtime with respect to the dataset size, and thus, these techniques
are rarely applicable to large volumes. In this work, we introduce a novel
clustering technique based on random sampling, which allows for the voxelwise
classification of arbitrarily large volumes. The presented method conducts
efficient linear passes over the data to extract a representative random sample
of a fixed size on which the classifier can be trained. Then, a final linear
pass performs the segmentation and assigns a cluster index to each individual
voxel. Quantitative and qualitative evaluations show that excellent results can
be achieved even with a very small sample size. Consequently, the unsupervised
segmentation by means of clustering becomes feasible for arbitrarily large
volumes.
