On the use of motion-based frame rejection in temporal averaging denoising for segmentation of echocardiographic image sequences

We have recently introduced an algorithm for semi-automatic segmentation of the left ventricular wall in short-axis echocardiographic images (EMBC 30:218–221). In its preprocessing stage, the algorithm uses temporal averaging for image denoising. Motion estimation is used to detect and reject frames that do not correlate well with the set of images being averaged. However, the process of estimating motion vectors is computationally intense, which increases the algorithm's computation time. In this work, we evaluate the viability of replacing the motion estimation stage with less computationally intense approaches. Two alternative techniques are evaluated. The ventricular contours obtained from each of the three algorithm variants were quantitatively and qualitatively compared with contours manually-segmented by a specialist. We show that it is possible to reduce the algorithm's computational load without significantly reducing the segmentation quality. The proposed algorithms are also compared with three other techniques from the literature.