Automated recognition of the pericardium contour on processed CT images using genetic algorithms
| Autor: | Érick Oliveira Rodrigues, Panos Liatsis, L. O. Rodrigues, Aura Conci, L. S. N. Oliveira |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Health Informatics 02 engineering and technology 030204 cardiovascular system & hematology Tracing Ellipse Machine Learning (cs.LG) Automation 03 medical and health sciences 0302 clinical medicine Genetic algorithm 0202 electrical engineering electronic engineering information engineering medicine FOS: Electrical engineering electronic engineering information engineering Humans Pericardium Neural and Evolutionary Computing (cs.NE) Medical diagnosis Metaheuristic business.industry Image and Video Processing (eess.IV) Process (computing) Computer Science - Neural and Evolutionary Computing Pattern recognition Image segmentation Anatomy Electrical Engineering and Systems Science - Image and Video Processing Computer Science Applications medicine.anatomical_structure Adipose Tissue cardiovascular system 020201 artificial intelligence & image processing Artificial intelligence Tomography X-Ray Computed business Algorithms |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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