Fully automatic left ventricular myocardial boundary detection in echocardiographic images: a comparison of two modern methods

The echo images in 2D echocardiography have poor noise characteristics and low spatial and grey-value resolutions. Numerous attempts have been made to develop automated algorithms for quantitative analysis and boundary extraction in these images, but as yet none have been developed adequately to be used clinically. This report presents two modern approaches for automatic extraction of the left ventricular (LV) epicardial and endocardial boundaries from short-axis (SA) echocardiographic data, and compares their performance. Both methods use the radial search algorithm in the extraction process. In the AMRBDS (automatic multiresolution boundary detection system), the first stage uses fuzzy logic and the spatial and intensity information of the input image to estimate the LV centre point (LVCP). Then, a novel multiresolution edge detection technique based on the wavelet transform is applied to each one of the radial intensity profiles to extract the most probable and unique LV edge points along them. Median post-filtering and cubic B-spline techniques are employed to produce the final LV boundaries. In the AANNBDS (automatic artificial neural networks boundary detection system), an MLP (multilayer perceptron) is used to detect the most appropriate centre point of the LV. A second MLP is trained to classify each pixel on the radial lines as an inner, outer or non-edge point. Finally, knowledge guided snakes are employed to extract the LV borders by minimization of the snakes' energy function.