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Heart attack is a leading cause of human deaths worldwide which occurs due to occlusion of coronary arteries and reduced blood flow. Hence, it requires early detection of disease in a non-invasive way through Cardiac Computed Tomography Angiogram images (CCTA). Local thresholding with Hessian matrix based Frangi's Vesselness filter has been applied for segmentation of coronary arterial branches. Stenosis as Region of Interest can be quantified in cardiac arteries by using Sobel Gradient Edge operator with threshold values 76 to 80. Mathematical model has been designed to measure the rate of change of blood flow in coronary arteries by adopting hemodynamic fluid mechanics using Hagen Poiseuille's law with Wall Shear Stress method. Computerized simulation results assist to detect healthy and cardiac diseased artery for better diagnosis.

iosr journal of computer engineering

Automated Segmentation of Cardiac Stenosis and Mathematical Modeling of Myocardial Blood Flow for Early Detection of Heart Attack Using Advanced Imaging Techniques