Wavelet-based Edge Detection Method for Analysis of Coronary Angiograms

The assessment of coronary anatomy is one of the prime determinants in choosing medical or interventional therapy for patients with ischemic heart disease. We report a wavelet based method of coronary border identification which has the advantage of the detection of the edges at different scales (the image changes are computed in a variable neighborhood), unlike the conventional methods where a fixed, heuristic neighborhood is used. Additionally, the conventional methods are more noise sensitive than a wavelet based method. We propose an algorithm to combine the information from the multiple scales. The computer determined diameters are compared to the actual diameters of the simulated vessels of three test objects. These comparisons show that our method allows accurate identification of the borders of phantom vessels (correlation coefficient in the range 0.90-0.99 for different wavelets).