The effect of initialization and registration on the active shape segmentation of the myocardium in contrast enhanced ultrasound
Ultrasound images inherently include speckle noise and other image artifacts resulting from various physical phenomena. Frequently, several ultrasound images are averaged together to improve image quality prior to automated processing. However, registration inaccuracy will produce blurred object edges and hamper the segmentation process. For automatic object identification in ultrasound imagery, model-based segmentation approaches are of particular interest because of their global approach and ability to infer edge-less parts of the object based on a priori information. These models are often implemented as active contours, which are iteratively deformed towards image objects. The associated segmentation results are strongly dependant on initial contour placement, as the contour often falls into local energy minima when initialized far from the desired image boundary. This paper presents results on the effects of initial contour placement and registration error on the success of an active contour model segmentation of the myocardium from contrast enhanced ultrasound. These data suggest that while a manual initialization provides superior segmentation results, a fully automatic segmentation approach is possible if some sacrifices are made in contour accuracy. Additionally, the registration error induced by the automatic registration algorithm presented here has no adverse effects on segmentation accuracy.