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

TL;DR

This paper introduces a genetic algorithm-based method for automatically detecting the pericardium contour in CT images, which can enhance diagnosis and fat quantification related to heart health.

Contribution

It presents a novel application of genetic algorithms to model the pericardium as an ellipse in CT slices, improving automation and accuracy over manual methods.

Findings

GA provides satisfactory contour detection accuracy

Method reduces manual effort in pericardium segmentation

Processing time is feasible for clinical use

Abstract

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.