Cardiac Cycle Phase Estimation in 2-D Echocardiographic Images Using an Artificial Neural Network

This paper proposes a new hybrid 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 by using the geometrical position of the mitral valve and a set of three image features. The proposed algorithm is based on a tandem of image processing methods and artificial neural networks as a classifier to robustly extract anatomical information. An original set of image features is proposed and derived to recognize the cardiac phases. 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. Our hybrid algorithm does not involve any manual tracing of the boundaries for segmentation process. The algorithm is implemented as computer-aided diagnosis (CADi) software. A dataset of 150 images that include both normal and infarct cardiac pathologies was used. We reported an accuracy of 90 % and a 2 ± 0.3 s in terms of execution time of CADi application in a cardiac cycle estimation task. The main contribution of this paper is to propose this hybrid method and a set of image features that can be helpful for automatic detection applications without any user intervention. The results of the employed methods are qualitatively and quantitatively compared in terms of efficiency for both scenarios.