1 March 1998 Curvilinear feature extraction from stacks of neuron images
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Proceedings Volume 3240, 26th AIPR Workshop: Exploiting New Image Sources and Sensors; (1998); doi: 10.1117/12.300051
Event: 26th AIPR Workshop: Exploiting New Image Sources and Sensors, 1997, Washington, DC, United States
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.
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Fenglian Xu, Paul H. Lewis, John E. Chad, Howard V. Wheal, "Curvilinear feature extraction from stacks of neuron images", Proc. SPIE 3240, 26th AIPR Workshop: Exploiting New Image Sources and Sensors, (1 March 1998); doi: 10.1117/12.300051; https://doi.org/10.1117/12.300051
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KEYWORDS
Neurons

Feature extraction

3D image processing

3D modeling

Brain

Confocal microscopy

Dendrites

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