Automatic vascular tree formation using the Mahalanobis distance.

Julien Jomier; Vincent LeDigarcher; Stephen R Aylward

doi:10.1007/11566489_99

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Automatic vascular tree formation using the Mahalanobis distance.

Julien Jomier·Vincent LeDigarcher·Stephen R Aylward

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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.

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Algorithms Angiography Artificial Intelligence Blood Vessels Humans Image Enhancement Image Interpretation, Computer-Assisted Imaging, Three-Dimensional Information Storage and Retrieval Pattern Recognition, Automated Reproducibility of Results Sensitivity and Specificity User-Computer Interface Evaluation Study Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

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Julien Jomier, Vincent LeDigarcher, Stephen R Aylward. 2005. Automatic vascular tree formation using the Mahalanobis distance.. https://doi.org/10.1007/11566489_99

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