Dimensionality reduction on multi-dimensional transfer functions for multi-channel volume data sets

  • Authors:

  • Han Suk Kim;Jürgen P. Schulze;Angela C. Cone;Gina E. Sosinsky;Maryann E. Martone

  • Affiliations:

  • Department of Computer Science and Engineering, University of California San Diego, La jolla, CA and National Center for Microscopy and Imaging Research, University of California San Diego, La jol ...;California Institute for Telecommunications and Information Technology, University of California San Diego, La jolla, CA;National Center for Microscopy and Imaging Research, University of California San Diego, La jolla, CA;National Center for Microscopy and Imaging Research, University of California San Diego, La jolla, CA;National Center for Microscopy and Imaging Research, University of California San Diego, La jolla, CA

  • Venue:
  • Information Visualization - Special issue on selected papers from visualization and data analysis 2010
  • Year:
  • 2010

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Abstract

The design of transfer functions for volume rendering is a nontrivial task. This is particularly true for multi-channel data sets, where multiple data values exist for each voxel, which require multi-dimensional transfer functions. In this article, we propose a new method for multi-dimensional transfer function design. Our new method provides a framework to combine multiple computational approaches and pushes the boundary of gradient-based multidimensional transfer functions to multiple channels, while keeping the dimensionality of transfer functions at a manageable level, that is, 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. Applying recently developed nonlinear dimensionality reduction algorithms reduce the high-dimensional data of the domain. In this article, we use Isomap and Locally Linear Embedding as well as a traditional algorithm, Principle Component Analysis. Our results show that these dimensionality reduction algorithms significantly improve the transfer function design process without compromising visualization accuracy. We demonstrate the effectiveness of our new dimensionality reduction algorithms with two volumetric confocal microscopy data sets.