Early detection of coronary artery blockage using image processing: segmentation, quantification, identification of degree of blockage and risk factors of heart attack

Abstract

Coronary artery blockage is a vital issue of occurring heart attack. There are several techniques to diagnose coronary artery blockage as well as other type heart diseases. In this paper, we discuss about computerized full automated model for the detection of coronary artery blockage using image processing techniques so that the system does not have to rely on human’s inspection. Using efficient image processing technique and AI algorithms, the system allows a faster and reliable detection of the narrowing area of the wall of coronary arteries due to the condensation of different artery blocking agents. The system requires a 64-slice/128-slice CTA image as input. After the acquisition of the desired input image, it goes through several steps to determine the region of interest. This research proposes a two stage approach that includes the pre-processing stage and decision stage. The pre-processing stage involves common image processing strategies while the decision stage involves the extraction and calculation of features to finally determine the intended result using AI algorithms. This type of model effectively enables early detection of coronary artery blockage through segmentation, quantification, identification of degree of blockage and risk factors of heart attack.