Left ventricular segmentation based on a parallel watershed transformation towards an accurate heart function evaluation
| Autor: | Rachida Saouli, Narjes Ben Ameur, Mohamed Akil, Asma Ammari, Ramzi Mahmoudi, Momahed Hedi Bedoui, Badii Hmida |
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| Přispěvatelé: | Laboratoire d'Informatique Gaspard-Monge (LIGM), Centre National de la Recherche Scientifique (CNRS)-Fédération de Recherche Bézout-ESIEE Paris-École des Ponts ParisTech (ENPC)-Université Paris-Est Marne-la-Vallée (UPEM), Algorithms, architectures, image analysis and computer graphics, Centre National de la Recherche Scientifique (CNRS)-Fédération de Recherche Bézout-ESIEE Paris-École des Ponts ParisTech (ENPC)-Université Paris-Est Marne-la-Vallée (UPEM)-Centre National de la Recherche Scientifique (CNRS)-Fédération de Recherche Bézout-ESIEE Paris-École des Ponts ParisTech (ENPC)-Université Paris-Est Marne-la-Vallée (UPEM), Laboratoire d'Informatique Intelligente (LINFI), Université Mohamed Khider de Biskra (BISKRA), Laboratoire Technologie et Imagerie Médicale [Monastir] (TIM), Faculté de Médecine de Monastir [Tunisie], Université Paris-Est Marne-la-Vallée (UPEM)-École des Ponts ParisTech (ENPC)-ESIEE Paris-Fédération de Recherche Bézout-Centre National de la Recherche Scientifique (CNRS), Université Paris-Est Marne-la-Vallée (UPEM)-École des Ponts ParisTech (ENPC)-ESIEE Paris-Fédération de Recherche Bézout-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Est Marne-la-Vallée (UPEM)-École des Ponts ParisTech (ENPC)-ESIEE Paris-Fédération de Recherche Bézout-Centre National de la Recherche Scientifique (CNRS) |
| Rok vydání: | 2020 |
| Předmět: |
Cardiac output
Computer science 02 engineering and technology 0202 electrical engineering electronic engineering information engineering medicine [INFO]Computer Science [cs] Segmentation Electrical and Electronic Engineering End-systolic volume Ejection fraction medicine.diagnostic_test business.industry 020206 networking & telecommunications Pattern recognition Magnetic resonance imaging Stroke volume Image segmentation medicine.anatomical_structure Ventricle Signal Processing cardiovascular system 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence business Software |
| Zdroj: | IET Image Processing IET Image Processing, Institution of Engineering and Technology, 2020, 14 (3), pp.506-517. ⟨10.1049/iet-ipr.2018.6379⟩ |
| ISSN: | 1751-9667 1751-9659 |
| DOI: | 10.1049/iet-ipr.2018.6379 |
| Popis: | Magnetic resonance imaging (MRI) has emerged as the golden reference for cardiac examination. This modality allows the assessment of human cardiovascular morphology, functioning, and perfusion. Although a couple of challenging issues, such as the cardiac magnetic resonance (MR) image's features and the large variability of images among several patients, still influences the cardiac cavities’ segmentation and needs to be carried out. In this study, the authors have profoundly reviewed and fully compared semi-automated segmentation methods performed on cardiac cine-MR short-axis images for the evaluation of the left ventricular functions. However, the number of parameters handled by the synthesised works is limited if not null. For the sake of ensuring the highest coverage of the left ventricle parameters computing, they have introduced a parallel watershed-based approach to segment the left ventricular allowing hence the computation of six parameters (end-diastolic volume, end-systolic volume, ejection fraction, cardiac output, stroke volume, and left ventricular mass). An algorithm is associated with the main considered measurements. The experimental results that were obtained through studying 20 patients’ MRI data base demonstrate their approach's accuracy in estimating real values of the parameters’ set thanks to a faithful segmentation of the myocardium. |
| Databáze: | OpenAIRE |
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