Early Detection and Segmentation of Coronary Artery Blockage using Advanced Imaging Modalities for Predicting Risk Factors of Heart Attack

Myocardial infarction, commonly known as heart attack, is a leading cause of human death world wide. Hence, it requires early detection of the disease non-invasively through real CT angiogram images. In this paper, median filter has been applied on input images for noise removal followed by global thresholding with frangi vesselness filter using dilation morphology for segmentation of coronary arteries. C ardiac stenosis, considered as ROI, has been detected by using canny edge gradient operator with threshold values 75 to 90. The proposed mathematical model explains rate of change in blood flow using fluid dynamic concept by Hagen Poiseuille law and vascular wall shear stress method for quantification of healthy and diseased coronary arteries. After that we have classified the levels of heart attack as intial, mild and severe using ANFIS (Adaptive Neuro Fuzzy Inference System) tool with membership function. Computer simulation results assist in predicting the risk factors of heart attack at an early stage.