An integrated approach to computer-based automated tracing and its validation for 200 common carotid arterial wall ultrasound images: a new technique.

OBJECTIVE: Most of the algorithms for the segmentation of the common carotid artery (CCA) wall require human interaction to locate the vessel in the ultrasound image. The aim of this article is to show an accurate algorithm for the computer-based automated tracing of the CCA in longitudinal B-mode ultrasound images.
METHODS: Two hundred images (100 normal CCAs, 50 CCAs with an increased intima-media thickness, 30 with fibrous plaques, and 20 with anechoic plaques) were processed to delineate the region of interest containing the CCA. The strategy is an integrated approach (carotid artery layer extraction using an integrated approach [CALEXia]) consisting of geometric feature extraction, line fitting, and classification. The output of the algorithm is the tracings of the proximal and distal adventitia layers. Performance of the algorithm was validated against human tracings considered the ground truth.
RESULTS: The mean distance errors +/- SD using this integrated approach were 1.05 +/- 1.04 pixels (0.07 +/- 0.07 mm) for proximal or near adventitia and 2.68 +/- 3.94 pixels (0.17 +/- 0.24 mm) for distal or far adventitia. Sixteen of 200 images were not perfectly traced because of the presence of both plaques and blood backscattering. The computational cost ensures the possibility for near real-time detection. Conclusions. Although the CALEXia algorithm automatically detects the CCA, it is also robust and validated over a large database. This can constitute a general basis for a completely automated segmentation procedure widely applicable to other anatomies.