Narrow Results

An efficient and robust method for identification of coronary arteries and evaluation of the severity of the stenosis on the routine X-ray angiograms is proposed. It is a challenging process to accurately identify coronary artery due to poor signal-to-noise ratio, vessel overlap, and superimposition with various anatomical structures such as ribs, spine, or heart chambers. The proposed method consists of two major stages: (a) signal-based image segmentation and (b) vessel feature extraction. The 3D Fourier and 3D Wavelet transforms are first employed to reduce the background and noisy structures in the images. Afterwards, a set of matched filters was applied to enhance the coronary arteries in the images. At the end, clustering analysis, histogram technique, and size filtering were utilized to obtain a binary image that consists of the final segmented coronary arterial tree. To extract vessel features in terms of vessel centerline and diameter, a gradient vector-flow based snake algorithm is applied to determine the medial axis of a vessel followed by the calculations of vessel boundaries and width associated with the detected medial axis.