Tree pruning strategy in automated detection of coronary trees in cineangiograms

The authors present a tree pruning strategy used in their novel approach to automated detection of coronary trees in cineangiograms. After the initial steps of the authors´ tree detection method, the preliminary tree contains a large number of false branches that must be removed by pruning. The tree pruning approach is based on information about branch absolute and relative lengths, gray level profile information, and information about the structure of the identified tree. The authors have assessed the performance of their tree pruning strategy in 13 coronary angiograms by comparison to manually identified coronary trees. After pruning, the percentage of false branches dropped from 87% to 8%. Computer-detected branch lengths correlated well with observer-identified branch lengths (r=0.95). Pruning substantially improved the accuracy of the detected trees