Robust seed detection for coronary arteries segmentation using thresholded Frangi response

Abstract

Automatic detection of initial seed points has become an essential step towards delineating coronary arteries in coronary computed tomography angiography (CCTA) images due to image inhomogeneity and other factors. Most coronary segmentation algorithms require user interaction for seed point selection, which may lead to erroneous segmentation. In this study, we present an improved technique of seed detection for coronary segmentation using a thresholded Frangi response. Before computing region of interest (ROI), the proposed method first computes the Frangi response of the complete CCTA volume, followed by thresholding with respect to quantile and median values, and then the ROI selection procedure is applied. Further, this procedure is joined with a feature that is built according to the resemblance among consecutive orthogonal cross-sections. The proposed method was evaluated on nine clinical datasets, and the proposed framework automatically detected coronary seeds accurately and can be used for an accurate delineation of coronary arteries. The obtained results were compared qualitatively and quantitatively by a radiologist, and the proposed method outperformed the previous method with an improvement of 45.9%.