Segmentation of Short Axis Cardiac MR Images using PCA with Active Appearance Model

Inamdar, R.

doi:10.9790/0661-0762530

Cited by 1 publication

(1 citation statement)

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“…In this study, we suggest to compare a set of existing methods [24–54] that can handle LV segmentation in a short‐MRI axis, and to choose the best one that assumes extracting and computing the maximum set of parameters. Moreover, we present a full‐process of LV myocardium segmentation from 3D + time cine‐MRI sequences based on a parallel watershed transformation [33].…”

Section: Introductionmentioning

confidence: 99%

Left ventricular segmentation based on a parallel watershed transformation towards an accurate heart function evaluation

Mahmoudi

Ameur

Ammari

et al. 2020

IET Image Processing

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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.

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Section: Introductionmentioning

confidence: 99%