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  • Automated analysis of multidimensional brain imagery

    Author(s)
    Tuxworth, Gervase
    Cavanagh, Brenton
    Meedeniya, Adrian
    Blumenstein, Michael
    Griffith University Author(s)
    Tuxworth, Gervase
    Year published
    2012
    Metadata
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    Abstract
    Neural cells are highly plastic, mirroring their functional state in their morphology. Data from classification of three dimensional images of individual cells would enable the functional state of the cell to be determined. Due to the complexity of the data, namely, the extreme irregularity in neural shape, existing three dimensional segmentation and feature extraction techniques do not perform well. The ever increasing dimensionality and quantity of image data also demands the image analysis process to be automated. To meet these goals, we have begun developing a fully automated image analysis technique, which allow us to ...
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    Neural cells are highly plastic, mirroring their functional state in their morphology. Data from classification of three dimensional images of individual cells would enable the functional state of the cell to be determined. Due to the complexity of the data, namely, the extreme irregularity in neural shape, existing three dimensional segmentation and feature extraction techniques do not perform well. The ever increasing dimensionality and quantity of image data also demands the image analysis process to be automated. To meet these goals, we have begun developing a fully automated image analysis technique, which allow us to quantitatively analyse neural cells in high resolution three dimensional images. We demonstrated the capacity of artificial neural networks to classify differing functional classes of neurons in our earlier work1. We have extended this work to automatically locate and segment cells from raw three dimensional image data.
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    Conference Title
    http://www.focusonmicroscopy.org/2012/index.html
    Publisher URI
    http://www.focusonmicroscopy.org/2012/index.html
    Subject
    Biochemistry and cell biology
    Neurosciences
    Publication URI
    http://hdl.handle.net/10072/389232
    Collection
    • Conference outputs

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