Automatic Vascular Tree Formation Using the Mahalanobis Distance

We present a novel technique for the automatic formation of vascular trees from segmented tubular structures. Our method combines a minimum spanning tree algorithm with a minimization criterion of the Mahalanobis distance. First, a multivariate class of connected junctions is defined using a set of trained vascular trees and their corresponding image volumes. Second, a minimum spanning tree algorithm forms the tree using the Mahalanobis distance of each connection from the "connected" class as a cost function. Our technique allows for the best combination of the discrimination criteria between connected and non-connected junctions and is also modality, organ and segmentation specific.