Profiling Peripheral Artery Structures Using CT Scan Data with Curve Planar Reformation and Gaussian Mixture Model in Polar Coordinates

Peripheral artery disease is a common circulatory disorder caused by the narrowing of arteries, which restricts blood flow to the extremities. Diagnosing this disease requires accurate and robust analytical methods due to the complicated structure of the arteries, their thin and complex nature, and their proximity to surrounding anatomical structures. In this study, an advanced method is proposed for analyzing peripheral artery structure using computed tomography images. The proposed framework utilizes of the polar coordinate representation and the Gaussian mixture model for clustering to effectively distinguish between different types of artery components. The results demonstrate its effectiveness in achieving accurate tissue differentiation, which can significantly enhance diagnostic tools for vascular imaging.