Intravascular ultrasound (IVUS) imaging is a catheter-based medical methodology establishing itself as a useful modality for\r\nstudying atherosclerosis. The detection of lumen and media-adventitia boundaries in IVUS images constitutes an essential step\r\ntowards the reliable quantitative diagnosis of atherosclerosis. In this paper, a novel scheme is proposed to automatically detect\r\nlumen and media-adventitia borders. This segmentation method is based on the level-set model and the contourletmultiresolution\r\nanalysis. The contourlet transform decomposes the original image into low-pass components and band-pass directional bands.\r\nThe circular hough transform (CHT) is adopted in low-pass bands to yield the initial lumen and media-adventitia contours.\r\nThe anisotropic diffusion filtering is then used in band-pass subbands to suppress noise and preserve arterial edges. Finally, the\r\ncurve evolution in the level-set functions is used to obtain final contours. The proposed method is experimentally evaluated via\r\n20 simulated images and 30 real images from human coronary arteries. It is demonstrated that the mean distance error and the\r\nrelative mean distance error have increased by 5.30 pixels and 7.45%, respectively, as compared with those of a recently traditional\r\nlevel-set model. These results reveal that the proposed method can automatically and accurately extract two vascular boundaries.
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