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Title: Quantification and Visualization of Variation in Anatomical Trees

This paper presents two approaches to quantifying and visualizing variation in datasets of trees. The first approach localizes subtrees in which significant population differences are found through hypothesis testing and sparse classifiers on subtree features. The second approach visualizes the global metric structure of datasets through low-distortion embedding into hyperbolic planes in the style of multidimensional scaling. A case study is made on a dataset of airway trees in relation to Chronic Obstructive Pulmonary Disease.
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Journal ID: 978-3-319-16347-5
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Resource Relation:
Journal Name: Proceedings from the Women in Shape Modeling Workshop, Institute for Pure and Applied Mathematics; Conference: Women in Shape Modeling Workshop, Institute for Pure and Applied Mathematics, July 2014
Research Org:
Nevada Test Site/National Security Technologies, LLC (United States)
Sponsoring Org:
USDOE National Nuclear Security Administration (NNSA)
Country of Publication:
United States
dataset, tree, quantification, visualization