The tree-edit-distance, a measure for quantifying neuronal morphology

Holger Heumann, Gabriel Wittum*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

The shape of neuronal cells strongly resembles botanical trees or roots of plants. To analyze and compare these complex three-dimensional structures it is important to develop suitable methods. We review the so called tree-edit-distance known from theoretical computer science and use this distance to define dissimilarity measures for neuronal cells. This measure intrinsically respects the tree-shape. It compares only those parts of two dendritic trees that have similar position in the whole tree. Therefore it can be interpreted as a generalization of methods using vector valued measures. Moreover, we show that our new measure, together with cluster analysis, is a suitable method for analyzing three-dimensional shape of hippocampal and cortical cells.

Original languageEnglish (US)
Pages (from-to)179-190
Number of pages12
JournalNeuroinformatics
Volume7
Issue number3
DOIs
StatePublished - Sep 1 2009

Keywords

  • Cluster analysis
  • Dissimilarity measure
  • Neuromorphometry
  • Tree-edit-distance

ASJC Scopus subject areas

  • Software
  • Neuroscience(all)
  • Information Systems

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