Purpose: The development of completely automated techniques for arterial wall segmentation and intima-media thickness measurement requires the recognition of the artery in the image frame. Conceptually, automated techniques can be thought of as the combination of two cascaded stages: artery recognition and wall segmentation. In this paper, the authors show three carotid artery recognition systems (CARS) that are fully automated.
Methods: The first technique is based on a first-order derivative Gaussian edge analysis (CARSgd). The second method is based on an integrated approach (CARSia) that combines image feature extraction, fitting, and classification. The third strategy is based on signal analysis (CARSsa). The output of all the three paradigms provide tracing of the far adventitial (AD(F)). The authors validated CARSgd, CARSia, and CARSsa on a dataset of 365 longitudinal B-Mode carotid images, acquired by different sonographers. Performance evaluation of the carotid recognition process was done in three ways: (1) visual inspection by experts; (2) by measuring the Hausdorff distance (HD) between the automatic far adventitial (AD(F)) and the manually traced AD(F), and (3) by measuring the HD between AD(F) and the lumen-intima (GT(LI)) and media-adventitia (GT(MA)) borders of the arterial walls.
Results: The average HD between AD(F) and the manual AD(F) was 1.53 ± 1.51 mm for CARSgd, 1.82 ± 3.08 mm for CARSia, and 2.56 ± 2.89 mm for CARSsa. The average HD between GT(LI) and AD(F) for CARSgd, CARSia, and CARSsa were 2.16 ± 1.16 mm, 2.71 ± 2.89 mm, and 2.66 ± 1.52 mm, respectively. The average HD between AD(F) and GT(MA) for CARSgd, CARSia, and CARSsa were 1.54 ± 1.19 mm, 1.86 ± 2.66 mm, and 1.95 ± 1.64 mm, respectively. Considering a maximum distance of 50 pixels (about 3 mm), CARSgd showed an identification accuracy of 100%, CARSia of 92%, and CARSsa of 96%. These identification accuracies were confirmed by visual inspection. All the three systems work on MATLAB, Windows OS, and on a PC based cross platform medical application written in Java called ATHEROEDGE™ with 1 s per image.
Conclusions: CARSgd showed very accurate AD(F) profiles coupled with a low computational burden and without the need for specific tuning. It can be thought of as a reference technique for carotid localization, to be used in automated intima-media thickness measurement strategies.