Quantification and Visualization of Variation in Anatomical Trees View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2015

AUTHORS

Nina Amenta , Manasi Datar , Asger Dirksen , Marleen de Bruijne , Aasa Feragen , Xiaoyin Ge , Jesper Holst Pedersen , Marylesa Howard , Megan Owen , Jens Petersen , Jie Shi , Qiuping Xu

ABSTRACT

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. More... »

PAGES

57-79

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-16348-2_5

DOI

http://dx.doi.org/10.1007/978-3-319-16348-2_5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1009544866


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