Automatic vascular tree formation using the mahalanobis distance

Authors:
Julien Jomier;Vincent LeDigarcher;Stephen R. Aylward
Affiliations:
Computer-Aided Diagnosis and Display Lab, Department of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill;Computer-Aided Diagnosis and Display Lab, Department of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill;Computer-Aided Diagnosis and Display Lab, Department of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill
Venue:
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Year:
2005

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Abstract

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.