slugDetermination of Atrial Diastole and Systole from 2-D Echocardiographic Images Using Artificial Neural Network
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2014, Vol 2, Issue 4
Abstract
This project proposes a new approach to estimate the cardiac cycle phases in 2-D echocardiographic images as a first step in cardiac volume estimation. We focused on analyzing the atrial systole and diastole events. The proposed metohd is based on a tandem of image processing methods and artificial neural networks as a classifier to robustly extract anatomical information. The aforementioned approach is performed in two denoising scenarios. In the first scenario, the images are corrupted with Gaussian noise, and in the second one with Rayleigh noise distribution. A dataset of 20 images that include both normal and infarct cardiac pathologies were used. The results of the employed methods are qualitatively and quantitatively compared in terms of efficiency for both scenarios. This method allows improving the time efficiency. In this method, feature extraction was assessed for both the analyzed cardiac phases, and then the images belonging to our database were classified by the instrumentality of an ANN. The cardiac cycle phase estimation is performed in apical twochamber long-axis 0◦ view (LAX0) of 2-D echocardiographic images. The experimental images were divided into two sets corresponding to two analyzed cardiac cycles (systole and diastole). The experimental echocardiographic images came from a blend of healthy and cardiac patients that suffer from myocardial infarction. Once artificial neural network is trained, the detection becomes very fast.
Authors and Affiliations
Asha Vincent, Reshmi Reji Jacob
Keywords
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