Artery skeleton extraction using topographic and connected component labeling

In this paper, we propose a method for the detection and extraction of coronary artery skeletons (centerlines) based on the morphological processing of the topographic features of coronary angiogram images. Initially, the angiogram is pre-processed for noise reduction and artery enhancement through directional morphological filtering by reconstruction. The topographic features of the resulting image are detected based on first and second-order image derivatives which characterize the local differential image structure. Using an artery model of a smooth elongated object with an approximately Gaussian smoothed semi-elliptical profile, the candidate skeleton areas are detected as sets of points consisting of ridges, saddle points and peaks. False skeleton areas, produced due to the noise sensitivity of the differentiation filters, have small size and are eliminated by connected component labeling (CCL). CCL may cause the elimination of a few true skeletons which are recovered by the morphological operation of binary reconstruction. Experimental results on clinical coronary angiograms are presented and discussed indicating the robust performance of the proposed method.