In many applications there is a need to visualize multidimensional data sets. Using spatial relationships alone, display is limited to two or three dimensions. The use of color, in addition to spatial relationships, increases the dimensionality of the data that can be effectively visualized. Use of color is usually achieved through the application of color tables. The generation of color tables is not an easy task, and since color space is relatively large it is nearly impossible for an individual to consider all of the possible options. A genetic algorithm was developed to automate this task, generating color tables for the joint display of high-resolution and dynamic contrast-enhanced magnetic resonance imaging and F-18-FDG positron emission tomography data sets. The results are promising, producing new color tables that meet defined requirements.
Karl G. Baum, Maria Helguera, Andrzej Krol, "Genetic Algorithm Automated Generation of Multivariate Color Tables for Visualization of Multimodal Medical Data Sets" in Proc. IS&T 14th Color and Imaging Conf., 2006, pp 138 - 143, https://doi.org/10.2352/CIC.2006.14.1.art00025