Volume Visualization Using Principal Component Analysis

Alakkari, Salaheddin; Dingliana, John

Volume Visualization Using Principal Component Analysis

Files

053-057.pdf (7.59 MB)

Date

2016

Authors

Alakkari, Salaheddin
Dingliana, John

Publisher

The Eurographics Association

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.

        @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
}
}