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 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 paper, we 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 synthesized works is limited if not null. For the sake of ensuring the highest coverage of the LV parameters computing, we have introduced a parallel watershedbased 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 main considered measurements. The experimental results that were obtained through studying twenty patients' MRI data base, demonstrate the accuracy of our approach for estimating real values of the maximal set of parameters thanks to a faithful segmentation of the myocardium.
The evaluation of Cardiac Magnetic Resonance (CMR) imaging exam is mainly based on the visual aspect. This visual evaluation depends on the level of expertise of the radiologist and it is characterized by variability within and between observers. The aim of this work is to propose a new method based on a mathematical model, "Fourier Transform" which calculates an amplitude parametric image. This image, calculated from the Cine MR images, allows the localization and quantification of abnormalities related to difference in contraction and their extent. The suggested amplitude image is likely to assist in the diagnosis through reducing the time taken by the radiologist to specify the abnormal contraction and by improving the accuracy of the examination. After testing this approach on patients (healthy and pathological), we have proven a good concordance between the results obtained by the parametric image and those collected from the routine examination.
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