Multi scale classification approach for coronary artery detection from X-ray angiography

X-ray angiography is currently the gold standard for navigation guidance during percutaneous coronary interventions. From X-ray angiography, robust automatic detection of coronary arteries would be of great interest during cardiac interventions. Multi scale Hessian-based filtering was proven successful to automatically detect vessels from X-ray angiography. However, other anatomical structures interfere greatly with the detection process and the result still contains many false positives. The goal of the project is to propose a novel machine learning-based method to improve Hessian-based coronary artery detection from X-ray angiography. The proposed method divides Hessian-filtered images in patches, uses feature extraction with a contour profiling algorithm, and classifies using Support Vector Machines. The method is applied recursively on the detected connected components using patches of different sizes to define the arteries. This scheme allows an improvement of robustness against noise and imaging artifacts.