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The tree-edit-distance, a measure for quantifying neuronal morphology
BMC Neuroscience volume 10, Article number: P89 (2009)
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.
Queisser G, Bading H, Wittmann M, Wittum G: Filtering, reconstruction and measurement of the geometry of neuron cell nuclei based on confocal microscopy data. Journal of Biomedical Optics. 2008
Voßen C, Eberhard J, Wittum G: Modeling and simulation for three-dimensional signal propagation in passive dendrites. Comput Vis Sci. 2007, 10:
Eberhard JP, Wanner A, Wittum G: NeuGen: A tool for the generation of realistic morphology of cortical neurons and neural networks in 3D. Neurocomputing. 2006, 70: 327-342. 10.1016/j.neucom.2006.01.028.
Broser PJ, Schulte R, Roth A, Helmchen F, Waters J, Lang S, Sakmann B, Wittum G: Nonlinear anisotropic diffusion filtering of three-dimensional image data from 2-photon microscopy. J Biomedical Optics. 2004, 9: 1253-1264. 10.1117/1.1806832.
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Heumann, H., Wittum, G. The tree-edit-distance, a measure for quantifying neuronal morphology. BMC Neurosci 10, P89 (2009) doi:10.1186/1471-2202-10-S1-P89
- Animal Model
- Cluster Analysis
- Computer Science
- Neuronal Cell
- Theoretical Computer