# Mathematical Morphology in Machine Learning

arXiv: https://arxiv.org/abs/2605.30700 · HTML: https://mlsanity.com/paper/2605.30700/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 29 May 2026.

## Builds on (most cited first, 6 of 6)

- [A novel approach for the automated segmentation and volume quantification of cardiac fats on computed tomography](https://mlsanity.com/paper/2112.11381/) (2021)
- [Combining Minkowski and Chebyshev: New distance proposal and survey of distance metrics using k-nearest neighbours classifier](https://mlsanity.com/paper/2112.12549/) (2021)
- [A simple approach for biometrics: Finger-knuckle prints recognition based on a Sobel filter and similarity measures](https://mlsanity.com/paper/2605.17673/) (2026)
- [Towards the automated segmentation of epicardial and mediastinal fats: A multi-manufacturer approach using intersubject registration and random forest](https://mlsanity.com/paper/2605.29217/) (2026)
- [On the Automated Segmentation of Epicardial and Mediastinal Cardiac Adipose Tissues Using Classification Algorithms](https://mlsanity.com/paper/2208.14352/) (2022)
- [Fractal triangular search: a metaheuristic for image content search](https://mlsanity.com/paper/2605.21559/) (2026)

## Abstract (arXiv)

This work introduces mathematical morphology-an established visual computing theory-into machine learning to exploit shape and density aspects often overlooked by standard techniques. We propose a fast clustering algorithm based on morphological reconstruction that accurately preserves cluster shapes and density. This scheme offers unique features: an intrinsic sense of maximal clusters, cost-free noise removal, and diverse growth patterns controlled by structuring elements.Additionally, we propose a novel distance metric combining Minkowski and Chebyshev distances, highly efficient for morphological dilations. In Z2 discrete neighbourhood iterations, it is roughly 1.3 times faster than Manhattan and 329.5 times faster than Euclidean distances. When evaluated using a k-Nearest Neighbours (k-NN) classifier across 33 UCI datasets against 14 other distances, our metric achieved above-average accuracies most frequently (26 of 33 cases) and the best overall accuracy in 9 cases.Finally, we introduce novel morphological classifiers. Unlike current literature, this proposal uniquely models shape, density, and fractal information in datasets.