Volume Visualization Using Principal Component Analysis

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Date
2016
Journal Title
Journal ISSN
Volume Title
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.
Description

        
@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
} }
Citation