Visualization of variations in human brain morphology using differentiating reflection functions

TL;DR

This paper investigates the use of differentiating reflection functions to enhance 2D visualization of 3D MRI brain data, aiming to better represent complex anatomical variations and genetic influences.

Contribution

It introduces a novel approach applying specific reflectance functions to MRI data to improve visualization of brain morphology variations.

Findings

Reflectance functions improve visualization clarity of brain structures.

Enhanced differentiation of anatomical segments in 2D images.

Potential for better understanding of genetic influences on brain morphology.

Abstract

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