Contrast enhancement methods have a long history of use in image processing for forensics and have been used to effect in the evaluation patterned injury of the skin. Most contrast enhancement methods, however, were developed for the evaluation of greyscale images and involve the manipulation of one dimension of data at a time. Contrast enhancement in a three- or more dimensional space poses challenges to the implementation of histogram equalization and similar algorithms. A number of approaches to dealing with this problem have been suggested, including performing operations on each channel independently or by various color `explosion' methods. Our laboratory has been investigating dispersion- and diffusion-based methods by modeling changes in color space as biological processes. In short, we model the migration and dispersion of points in color space as migration and differentiation. In this model, biological differentiation signals are used for segmentation in color space (color quantization) and chemoattractant and diffusion models are used for swarming and dispersal. The results of this method are compared with more traditional methods. Implementation issues are discussed. Extensions to the use of reaction-diffusion equations for color-space segmentation are discussed.