Automatic detection of the carotid artery boundary on cross-sectional MR image sequences using a circle model guided dynamic programming

BACKGROUND: Systematic aerobe training has positive effects on the compliance of dedicated arterial walls. The adaptations of the arterial structure and function are associated with the blood flow-induced changes of the wall shear stress which induced vascular remodelling via nitric oxide delivered from the endothelial cell. In order to assess functional changes of the common carotid artery over time in these processes, a precise measurement technique is necessary. Before this study, a reliable, precise, and quick method to perform this work is not present. METHODS: We propose a fully automated algorithm to analyze the cross-sectional area of the carotid artery in MR image sequences. It contains two phases: (1) position detection of the carotid artery, (2) accurate boundary identification of the carotid artery. In the first phase, we use intensity, area size and shape as features to discriminate the carotid artery from other tissues and vessels. In the second phase, the directional gradient, Hough transform, and circle model guided dynamic programming are used to identify the boundary accurately. RESULTS: We test the system stability using contrast degraded images (contrast resolutions range from 50% to 90%). The unsigned error ranges from 2.86% ± 2.24% to 3.03% ± 2.40%. The test of noise degraded images (SNRs range from 16 to 20 dB) shows the unsigned error ranging from 2.63% ± 2.06% to 3.12% ± 2.11%. The test of raw images has an unsigned error 2.56% ± 2.10% compared to the manual tracings. CONCLUSIONS: We have proposed an automated system which is able to detect carotid artery cross sectional boundary in MRI sequences during heart cycles. The accuracy reaches 2.56% ± 2.10% compared to the manual tracings. The system is stable, reliable and results are reproducible.