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    Multichannel transfer function with dimensionality reduction

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    Type
    Conference Paper
    Authors
    Kim, Han Suk
    Schulze, Jürgen P.
    Cone, Angela C.
    Sosinsky, Gina E.
    Martone, Maryann E.
    KAUST Grant Number
    US 2008-107
    Date
    2010-01-17
    Permanent link to this record
    http://hdl.handle.net/10754/598904
    
    Metadata
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    Abstract
    The design of transfer functions for volume rendering is a difficult task. This is particularly true for multi-channel data sets, where multiple data values exist for each voxel. In this paper, we propose a new method for transfer function design. Our new method provides a framework to combine multiple approaches and pushes the boundary of gradient-based transfer functions to multiple channels, while still keeping the dimensionality of transfer functions to a manageable level, i.e., a maximum of three dimensions, which can be displayed visually in a straightforward way. Our approach utilizes channel intensity, gradient, curvature and texture properties of each voxel. The high-dimensional data of the domain is reduced by applying recently developed nonlinear dimensionality reduction algorithms. In this paper, we used Isomap as well as a traditional algorithm, Principle Component Analysis (PCA). Our results show that these dimensionality reduction algorithms significantly improve the transfer function design process without compromising visualization accuracy. In this publication we report on the impact of the dimensionality reduction algorithms on transfer function design for confocal microscopy data.
    Citation
    Kim HS, Schulze JP, Cone AC, Sosinsky GE, Martone ME (2010) Multichannel transfer function with dimensionality reduction. Visualization and Data Analysis 2010. Available: http://dx.doi.org/10.1117/12.839526.
    Sponsors
    This publication was made possible by Grant Number (NCRR P41-RR004050) from the National Center forResearch Resources (NCRR), a part of the National Institutes of Health (NIH). Its contents are solely the responsibilityof the authors and do not necessarily represent the official views of the NIH. This publication is basedin part on work supported by Award No. US 2008-107, made by King Abdullah University of Science and Technology(KAUST), by NIH Award (NIGMS F32GM092457) and by National Science Foundation Awards (NSFMCB-0543934 and OCE-0835839). Finally, the authors would like to thank Lawrence Saul and the anonymousreviewers for their helpful comments.
    Publisher
    SPIE-Intl Soc Optical Eng
    Journal
    Visualization and Data Analysis 2010
    DOI
    10.1117/12.839526
    PubMed ID
    20582228
    PubMed Central ID
    PMC2891081
    ae974a485f413a2113503eed53cd6c53
    10.1117/12.839526
    Scopus Count
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