A New Approach of Arc Skeletonization for Tree-like Objects Using Minimum Cost Path

Traditional arc skeletonization algorithms using the principle of Blum´s transform, often, produce unwanted spurious branches due to boundary irregularities and digital effects on objects and other artifacts. This paper presents a new robust approach of extracting arc skeletons for three-dimensional (3-D) elongated fuzzy objects, which avoids spurious branches without requiring post-pruning. Starting from a root voxel, the method iteratively expands the skeleton by adding a new branch in each iteration that connects the farthest voxel to the current skeleton using a minimum-cost geodesic path. The path-cost function is formulated using a novel measure of local significance factor defined by fuzzy distance transform field, which forces the path to stick to the centerline of the object. The algorithm terminates when dilated skeletal branches fill the entire object volume or the current farthest voxel fails to generate a meaningful branch. Accuracy of the algorithm has been evaluated using computer-generated blurred and noisy phantoms with known skeletons. Performance of the method in terms of false and missing skeletal branches, as defined by human expert, has been examined using in vivo CT imaging of human intrathoracic airways. Experimental results from both experiments have established the superiority of the new method as compared to a widely used conventional method in terms of accuracy of medialness as well as robustness of true and false skeletal branches.