title: The tree-edit-distance, a measure for quantifying neuronal morphology.
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.
authors.affiliation: SAM, ETH Zürich, Rämistrasse 101, 8092 Zürich, Switzerland. hheumann@math.ethz.ch
authors.forename: Holger
authors.initials: H
authors.lastname: Heumann
authors.forename: Gabriel
authors.initials: G
authors.lastname: Wittum
mesh_terms: D000465:Algorithms; D000818:Animals; D048430:Cell Shape; D002540:Cerebral Cortex / Q000166:cytology / Q000502:physiology; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D019992:Databases as Topic; D003712:Dendrites / Q000502:physiology / Q000648:ultrastructure; D006624:Hippocampus / Q000166:cytology / Q000502:physiology; D006801:Humans; D019044:Image Cytometry / Q000379:methods*; D008432:Mathematical Computing; D009474:Neurons / Q000166:cytology* / Q000502:physiology; D012984:Software