# X-Ray cardiac angiographic vessel segmentation based on pixel classification using machine learning and region growing

arXiv: https://arxiv.org/abs/2605.20073 · HTML: https://mlsanity.com/paper/2605.20073/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 53 references that are in the map.
- First posted on arXiv 19 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)
- [Morphological classifiers](https://mlsanity.com/paper/2112.12262/) (2021)
- [k-MS: A novel clustering algorithm based on morphological reconstruction](https://mlsanity.com/paper/2208.14390/) (2022)
- [A simple approach for biometrics: Finger-knuckle prints recognition based on a Sobel filter and similarity measures](https://mlsanity.com/paper/2605.17673/) (2026)
- [Machine learning in the prediction of cardiac epicardial and mediastinal fat volumes](https://mlsanity.com/paper/2208.14374/) (2022)

## Abstract (arXiv)

This work proposes a pixel-classification approach for vessel segmentation in x-ray angiograms. The proposal uses textural features such as anisotropic diffusion, features based on the Hessian matrix, mathematical morphology and statistics. These features are extracted from the neighborhood of each pixel. The approach also uses the ELEMENT methodology, which consists of creating a pixel-classification controlled by region-growing where the result of the classification affects further classifications of pixels. The Random Forests classifier is used to predict whether the pixel belongs to the vessel structure. The approach achieved the best accuracy in the literature (95.48%) outperforming unsupervised state-of-the-art approaches.