Blockage in Coronary Artery Detection and Quantification in Coronary Angiography

Abstract

The segmentation of the coronary angiography is extremely crucial in computer-aided diagnosis of arterial motion evaluation. It is difficult to create an automated vessel segmentation method using vascular structures because of the wide range of intensities and noise. The suggested method is an unsupervised method that uses coronary angiogram of the heart as a source and in order to get vascular centerlines, segment vessels, and identify heart vein blockages. First, a preprocessing procedure is utilised to enhance and get rid of the image's low frequency noise using morphological filters and a contrast constrained adaptive histogram equalisation. The extraction of the vascular structure is done using a morphological hessian-based method. The wide and narrow vessels are removed using two distinct scales. After that, the vessel's axis of rotation is extracted. In order to find the bifurcation, it employs a branch detection algorithm. Obstructions are located by considering the diameter across the vessel's cross section.  The efficiency of the suggested method has been evaluated, as evidenced by testing results on a variety of images, achieving an accuracy of 97.08%.