Conventional visualization media such as MRI prints and computer screens are inherently two dimensional, making them incapable of displaying true 3D volume data sets. By applying only transparency or intensity projection, and ignoring light-matter interaction, results will likely fail to give optimal results. Little research has been done on using reflectance functions to visually separate the various segments of a MRI volume. We will explore if applying specific reflectance functions to individual anatomical structures can help in building an intuitive 2D image from a 3D dataset. We will test our hypothesis by visualizing a statistical analysis of the genetic influences on variations in human brain morphology because it inherently contains complex and many different types of data making it a good candidate for our approach
@article{arxiv.cs/0311034,
title = {Visualization of variations in human brain morphology using differentiating reflection functions},
author = {Gibby Koldenhof},
journal= {arXiv preprint arXiv:cs/0311034},
year = {2007}
}