Generalized Step Criterion Edge Detectors for Kalman Filter Based Left Ventricle Tracking in 3D+T Echocardiography

Abstract

Step criterion edge detector (STEP) has been employed for the detection of endocardial edges in a Kalman filter based left ventricle tracking framework in previous studies. STEP determines the endocardial edge positions by fitting piecewise constant functions to intensity profiles, which are extracted on a tracked surface’s normal directions. In this study, we generalize STEP using higher order piecewise polynomial functions. The generalized STEP detectors make different assumptions about the endocardial edge representations, and their accuracies vary over the endocardial surface and cardiac cycle positions. Accordingly, we combine the responses of the generalized detectors using a maximum likelihood (ML) approach. Unlike previously proposed ML approaches, our combined edge detector provides a real-time tracking solution as the majority of regressive functions for the polynomial fitting can be computed offline. Comparative analyses showed that the combined detector (1) outperforms each of the generalized STEP detectors, and (2) provides a comparable accuracy with the previously defined slower ML approach.