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.
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Jomier, J., LeDigarcher, V., Aylward, S.R. (2005). Automatic Vascular Tree Formation Using the Mahalanobis Distance. In: Duncan, J.S., Gerig, G. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2005. MICCAI 2005. Lecture Notes in Computer Science, vol 3750. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11566489_99
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DOI: https://doi.org/10.1007/11566489_99
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29326-2
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