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 (ADF). 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 (ADF) and the manually traced ADF, and (3) by measuring the HD between ADF and the lumen-intima (GTLI) and media-adventitia (GTMA) borders of the arterial walls.Results:
The average HD between ADF and the manual ADF 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 GTLI and ADF 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 ADF and GTMA 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 ADF 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.
II. MATERIALS AND METHODS
II.A. Imagedatabase and preprocessing steps
II.B. CARSgd: Far adventitia border detection based on first-order derivative Gaussian edge analysis
II.C. CARSia: Far adventitia border detection using feature extraction and fitting
II.d. CARSsa: Local statistics approach
II.E. Hausdorff distance metric: How good is the carotid artery recognition?
II.F. IMT measurement by first-order absolute moment (FOAM) operator
IV. DISCUSSION AND CONCLUSIONS
IV.A. Rationale for using the Hausdorff distance
IV.B. Calibration factor
IV.C. Possible inaccuracy sources and developed strategic solutions
IV.D. Robustness to noise
IV.E. Comparison with other methods
- Vascular system
- Medical imaging
- Stimulated Raman scattering
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