Literature DB >> 16685829

Spatial graphs for intra-cranial vascular network characterization, generation, and discrimination.

Stephen R Aylward1, Julien Jomier, Christelle Vivert, Vincent LeDigarcher, Elizabeth Bullitt.   

Abstract

Graph methods that summarize vasculature by its branching topology are not sufficient for the statistical characterization of a population of intra-cranial vascular networks. Intra-cranial vascular networks are typified by topological variations and long, wandering paths between branch points. We present a graph-based representation, called spatial graphs, that captures both the branching patterns and the spatial locations of vascular networks. Furthermore, we present companion methods that allow spatial graphs to (1) statistically characterize populations of vascular networks, (2) generate the central vascular network of a population of vascular networks, and (3) distinguish between populations of vascular networks. We evaluate spatial graphs by using them to distinguish the gender and handedness of individuals based on their intra-cranial vascular networks.

Mesh:

Year:  2005        PMID: 16685829     DOI: 10.1007/11566465_8

Source DB:  PubMed          Journal:  Med Image Comput Comput Assist Interv


  2 in total

1.  Studying cerebral vasculature using structure proximity and graph kernels.

Authors:  Roland Kwitt; Danielle Pace; Marc Niethammer; Stephen Aylward
Journal:  Med Image Comput Comput Assist Interv       Date:  2013

2.  Automated method for identification and artery-venous classification of vessel trees in retinal vessel networks.

Authors:  Vinayak S Joshi; Joseph M Reinhardt; Mona K Garvin; Michael D Abramoff
Journal:  PLoS One       Date:  2014-02-12       Impact factor: 3.240

  2 in total

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