Automatic Vasculature Identification in Coronary Angiograms by Adaptive Geometrical Tracking

Abstract

As the uneven distribution of contrast agents and the perspective projection principle of X-ray, the vasculatures in angiographic image are with low contrast and are generally superposed with other organic tissues; therefore, it is very difficult to identify the vasculature and quantitatively estimate the blood flow directly from angiographic images. In this paper, we propose a fully automatic algorithm named adaptive geometrical vessel tracking (AGVT) for coronary artery identification in X-ray angiograms. Initially, the ridge enhancement (RE) image is obtained utilizing multiscale Hessian information. Then, automatic initialization procedures including seed points detection, and initial directions determination are performed on the RE image. The extracted ridge points can be adjusted to the geometrical centerline points adaptively through diameter estimation. Bifurcations are identified by discriminating connecting relationship of the tracked ridge points. Finally, all the tracked centerlines are merged and smoothed by classifying the connecting components on the vascular structures. Synthetic angiographic images and clinical angiograms are used to evaluate the performance of the proposed algorithm. The proposed algorithm is compared with other two vascular tracking techniques in terms of the efficiency and accuracy, which demonstrate successful applications of the proposed segmentation and extraction scheme in vasculature identification.

Automated neurovascular tracing and analysis of the knife-edge scanning microscope Rat Nissl data set using a computing cluster

“…Xiao et al [36] used ridge enhancement in order to emphasize vascular structure. Then, seed points were selected within emphasized vascular structure in terms of its local maxima intensity level when its hessian matrix stayed negative, and its gradient remained as zero.…”

Respiratory-synchronized digital subtraction angiography based on a respiratory phase matching method

“…Unfortunately, the computational times were unrealistic in all those reported strategies. As an alternative, numerous studies have proposed extraction of the vessel structures only from angiographic images [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. In those studies, the centerlines were tracked or vessel regions were segmented from angiographic images.…”

Automatic segmentation of vessels from angiogram sequences using adaptive feature transformation

“…Therefore, this paper proposes a method that can accurately segment vessels under such complex conditions. Several proposed vessel segmentation methods can be classified as tracking-based [1][2][3][4][5], model-based [6][7][8][9][10][11][12], and pattern recognition [13][14][15][16][17][18][19][20][21][22][23][24]. Tracking-based methods entail using local operators to detect vessels.…”

“…However, the method cannot extract vessel features because of the low contrast and noise. An adaptive geometrical vessel tracking method [5] was proposed, entailing an algorithm that first detects seed points in an enhanced image and then extracts vessel points from the original angiogram based on the geometric features. Vessel points were recursively detected in scanlines, which were adjusted according to the estimated vessel diameters.…”