# Clustering large 3D volumes: A sampling-based approach

arXiv: https://arxiv.org/abs/2303.04188 · HTML: https://mlsanity.com/paper/2303.04188/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 7 Mar 2023.

## Builds on (most cited first, 3 of 3)

- [Adversarial Laws of Large Numbers and Optimal Regret in Online Classification](https://mlsanity.com/paper/2101.09054/) (2021)
- [Approximating Spectral Clustering via Sampling: a Review](https://mlsanity.com/paper/1901.10204/) (2019)
- [Sampling Algorithms, from Survey Sampling to Monte Carlo Methods: Tutorial and Literature Review](https://mlsanity.com/paper/2011.00901/) (2020)

## Abstract (arXiv)

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.