Automatic detection of the carotid lumen axis in B-mode ultrasound images.

A new approach is introduced for the automatic detection of the lumen axis of the common carotid artery in B-mode ultrasound images. The image is smoothed using a Gaussian filter and then a dynamic programming scheme extracts the dominant paths of local minima of the intensity and the dominant paths of local maxima of the gradient magnitude with the gradient pointing downwards. Since these paths are possible estimates of the lumen axis and the far wall of a blood vessel, respectively, they are grouped together into pairs. Then, a pattern of two features is computed from each pair of paths and used as input to a linear discriminant classifier in order to select the pair of paths that correspond to the common carotid artery. The estimated lumen axis is the path of local minima of the intensity that belongs to the selected pair of paths. The proposed method is suited to real time processing, no user interaction is required and the number of parameters is minimal and easy to determine. The validation was performed using two datasets, with a total of 199 images, and has shown a success rate of 99.5% (100% if only the carotid regions for which a ground truth is available are considered). The datasets have a large diversity of images, including cases of arteries with plaque and images with heavy noise, text or other graphical markings inside the artery region.