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    Volume Visualization Using Principal Component Analysis

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    Date
    2016
    Author
    Alakkari, Salaheddin
    Dingliana, John
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    Abstract
    In this paper, we investigate the use of Principal Component Analysis (PCA) for image-based volume visualization. Firstly we compute a high-dimensional eigenspace using training images, pre-rendered using a standard ray-caster, from a spherically distributed range of camera positions. Then, our system is able to synthesize arbitrary views of the dataset with minimal computation at runtime. We propose a perceptually-adaptive technique to minimize data size and computational complexity whilst preserving perceptual quality of the visualization, in comparison to corresponding ray-cast images. Results indicate that PCA is able to sufficiently learn the full view-independent volumetric model through a finite number of training images and generalize the computed eigenspace to produce high quality images from arbitrary viewpoints, on demand. The approach has potential application in client-server volume visualization or where results of a computationally-complex 3D imaging process need to be interactively visualized on a display device of limited specification.
    BibTeX
    @inproceedings {10.2312:vcbm.20161271,
    booktitle = {Eurographics Workshop on Visual Computing for Biology and Medicine},
    editor = {Stefan Bruckner and Bernhard Preim and Anna Vilanova and Helwig Hauser and Anja Hennemuth and Arvid Lundervold},
    title = {{Volume Visualization Using Principal Component Analysis}},
    author = {Alakkari, Salaheddin and Dingliana, John},
    year = {2016},
    publisher = {The Eurographics Association},
    ISSN = {2070-5786},
    ISBN = {978-3-03868-010-9},
    DOI = {10.2312/vcbm.20161271}
    }
    URI
    http://dx.doi.org/10.2312/vcbm.20161271
    https://diglib.eg.org:443/handle/10.2312/vcbm20161271
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    Eurographics Association copyright © 2013 - 2023 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA