Border identification of echocardiograms via multiscale edge detection and shape modeling

Abstract

An algorithm for endocardial and epicardial border identification of the left ventricle in 2-D short-axis echocardiographic images is presented. Our approach relies on shape modeling of endocardial and epicardial boundaries and prominent border information extracted from image sequences. The algorithm consists of four steps; wavelet-based edge detection, border segment extraction, border reconstruction, and boundary smoothing. Wavelet maximum representation of edges, dynamic shape modeling and matched filtering techniques are utilized to determine the center point of the left ventricle, and carry out feature extraction of border segments to better approximate endocardial and epicardial boundaries. The algorithm can reliably estimate the center point of the left ventricle, and also determine both endocardial and epicardial boundaries. Myocardial boundary identification is autonomous requiring no human input for initial estimation of boundary locations. Sample experimental results are shown for endocardial and epicardial border identification in 2-D short-axis echocardiograms.