New approach for quantitative coronary analysis (QCA) tool using multiresolution edge detection technique

Abstract

Variations of vessel's sizes, inter and intra-observer variability, nontrivial noise distribution, and the fuzzy representation of vessel's parameters are issues of concern for enhancing precision and accuracy of the available QCA techniques. In this paper, we present new multiresolution edge detection algorithm for determining vessel boundaries, and enhancing their centerline features. A bank of Canny filters of different resolutions is created. These filters are convolved with vascular images in order to obtain an edge image. Each filter will give maximum response to the segment of vessel having the same spatial resolution as the filter. The resulting responses across filters of different resolutions are combined to create an edge map for edge optimization. Boundaries of vessels are represented by edge-lines and are optimized on filter outputs with dynamic programming. The determined edge-lines are used to create vessel centerline. The centerline is then used to compute percent-diameter stenosis and coronary lesions. The system has been validated using synthetic images, flexible tube phantoms, and real angiograms. It has also been tested on coronary lesions with independent operators for inter-operator and intra-operator variability and reproducibility. The system has been found to be especially robust in complex images involving vessel branching and incomplete contrast filling.