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Principal Component Analysis Techniques for Visualization of Volumetric Data

  • Salaheddin Alakkari
  • John Dingliana
Chapter

Abstract

We investigate the use of Principal Component Analysis (PCA) for the visualization of 3D volumetric data. For static volume datasets, we assume, as input training samples, a set of images rendered from spherically distributed viewing positions, using a state-of-the-art volume rendering technique. We compute a high-dimensional eigenspace, that we can then use to synthesize arbitrary views of the dataset with minimal computation at run-time. Visual quality is improved by subdividing the training samples using two techniques: cell-based decomposition into equally sized spatial partitions and a more generalized variant, which we referred to as band-based PCA. The latter approach is further extended for the compression of time-varying volume data directly. This is achieved by taking, as input, full 3D volumes comprised by the time-steps of the time-varying sequence and generating an eigenspace of volumes. Results indicate that, in both cases, PCA can be used for effective compression with minimal loss of perceptual quality, and could benefit applications such as client-server visualization systems.

Notes

Acknowledgements

This research has been conducted with the financial support of Science Foundation Ireland (SFI) under Grant Number 13/IA/1895. The VisMale Head dataset is courtesy of the Visible Human Project at the U.S. National Library of Medicine. The Tooth was obtained from the Volume Library of Stefan Roettger. The Chest was obtained from the DICOM sample datasets provided by OsiriX. The Supernova data set is made available by Dr. John Blondin at the North Carolina State University through US Department of Energy’s SciDAC Institute for Ultrascale Visuaization. The Turbulent Vortex dataset obtained from Time Varying Volume Data Reporsitory at UC Davis.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Graphics Vision and Visualisation Group, School of Computer Science and StatisticsTrinity CollegeDublinIreland

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