A Fully Automated Approach to Preprocessing and Segmentation of Coronary Arteries in X-ray Angiography Images

Abstract

In medical practice, X-ray coronary angiography (XRA) represents a gold standard in diagnosing coronary artery (CA) disease. Although deep learning methods achieve high accuracy results, large number of labeled imaging data are often unavailable. As a result, unsupervised methods based on filters should be prioritized. This paper focuses on methodologies for tackling preprocessing steps and segmentation of coronary arteries in X-ray angiography images, only to improve the 3D reconstruction of the CA in the later stages. Dataset with X-ray coronary angiography images of 147 patients from a local database was used for segmentation of left and right coronary artery. Several preprocessing steps after which ridge and edge detection and global Otsu’s thresholding was applied, showed that several techniques can be applied in order to detect coronary arteries without unwanted noise, additional details and with connectivity among detected centerlines. The results of this study will represent and input to further steps included on 3D reconstruction of coronary arteries.