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Proceedings Paper

Curvilinear feature extraction from stacks of neuron images
Author(s): Fenglian Xu; Paul H. Lewis; John E. Chad; Howard V. Wheal
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Paper Abstract

A new approach is proposed for extracting explicit representations of 3D curvilinear features form stacks of 2D images. The images, which are of brain tissue, were obtained by confocal microscopy and the features represent the dendritic tree structure surrounding a neuron. Voxels with a high probability of being on the center-lines of the dendrites are identified first. Then a combination of a 3D minimum spanning tree and a 3D minimum cost path algorithm is used to automatically extract explicit center-line representations of the curvilinear features. The final objective of the image analysis is to produce, as automatically as possible, generalized cylinder models of the dendritic structures which are then used for studying neuronal morphology and function. In this paper, we concentrate on the algorithms used to extract the center- line representation.

Paper Details

Date Published: 1 March 1998
PDF: 10 pages
Proc. SPIE 3240, 26th AIPR Workshop: Exploiting New Image Sources and Sensors, (1 March 1998); doi: 10.1117/12.300051
Show Author Affiliations
Fenglian Xu, Univ. of Southampton (United Kingdom)
Paul H. Lewis, Univ. of Southampton (United Kingdom)
John E. Chad, Univ. of Southampton (United Kingdom)
Howard V. Wheal, Univ. of Southampton (United Kingdom)

Published in SPIE Proceedings Vol. 3240:
26th AIPR Workshop: Exploiting New Image Sources and Sensors
J. Michael Selander, Editor(s)

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