# k-MS: A novel clustering algorithm based on morphological reconstruction

arXiv: https://arxiv.org/abs/2208.14390 · HTML: https://mlsanity.com/paper/2208.14390/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 36 references that are in the map.
- First posted on arXiv 30 Aug 2022.

## Built on by (most cited first, 5 of 5)

- [ELEMENT: Multi-Modal Retinal Vessel Segmentation Based on a Coupled Region Growing and Machine Learning Approach](https://mlsanity.com/paper/2605.20458/) (2026)
- [Line Art Colorization of Fakemon using Generative Adversarial Neural Networks](https://mlsanity.com/paper/2307.05760/) (2023)
- [Proposal and study of statistical features for string similarity computation and classification](https://mlsanity.com/paper/2605.15110/) (2026)
- [X-Ray cardiac angiographic vessel segmentation based on pixel classification using machine learning and region growing](https://mlsanity.com/paper/2605.20073/) (2026)
- [Machine learning applied to emerald gemstone grading: framework proposal and creation of a public dataset](https://mlsanity.com/paper/2605.23777/) (2026)

## Abstract (arXiv)

This work proposes a clusterization algorithm called k-Morphological Sets (k-MS), based on morphological reconstruction and heuristics. k-MS is faster than the CPU-parallel k-Means in worst case scenarios and produces enhanced visualizations of the dataset as well as very distinct clusterizations. It is also faster than similar clusterization methods that are sensitive to density and shapes such as Mitosis and TRICLUST. In addition, k-MS is deterministic and has an intrinsic sense of maximal clusters that can be created for a given input sample and input parameters, differing from k-Means and other clusterization algorithms. In other words, given a constant k, a structuring element and a dataset, k-MS produces k or less clusters without using random/ pseudo-random functions. Finally, the proposed algorithm also provides a straightforward means for removing noise from images or datasets in general.