# Automated recognition of the pericardium contour on processed CT images using genetic algorithms

arXiv: https://arxiv.org/abs/2208.14375 · HTML: https://mlsanity.com/paper/2208.14375/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 38 references that are in the map.
- First posted on arXiv 30 Aug 2022.

## Builds on (most cited first, 1 of 1)

- [A novel approach for the automated segmentation and volume quantification of cardiac fats on computed tomography](https://mlsanity.com/paper/2112.11381/) (2021)

## Built on by (most cited first, 2 of 2)

- [Line Art Colorization of Fakemon using Generative Adversarial Neural Networks](https://mlsanity.com/paper/2307.05760/) (2023)
- [Water and Electricity Consumption Forecasting at an Educational Institution using Machine Learning models with Metaheuristic Optimization](https://mlsanity.com/paper/2410.19709/) (2024)

## Abstract (arXiv)

This work proposes the use of Genetic Algorithms (GA) in tracing and recognizing the pericardium contour of the human heart using Computed Tomography (CT) images. We assume that each slice of the pericardium can be modelled by an ellipse, the parameters of which need to be optimally determined. An optimal ellipse would be one that closely follows the pericardium contour and, consequently, separates appropriately the epicardial and mediastinal fats of the human heart. Tracing and automatically identifying the pericardium contour aids in medical diagnosis. Usually, this process is done manually or not done at all due to the effort required. Besides, detecting the pericardium may improve previously proposed automated methodologies that separate the two types of fat associated to the human heart. Quantification of these fats provides important health risk marker information, as they are associated with the development of certain cardiovascular pathologies. Finally, we conclude that GA offers satisfiable solutions in a feasible amount of processing time.