Cellular automata for the analysis of biomedical hyperspectral images

In this paper, we describe a technique whereby cellular automata are used to rapidly scan hyperspectral medical images and quantify the extent of conditions of medical interest. The cellular automata population uses the condition of interest as 'food' and only grows in those areas of the image where the food is present. The size of the cellular automata population can be correlated with the fractional area of the image containing the condition of interest. The technique has the potential to significantly reduce the computational overhead required to analyze a hyperspectral image. A simple model of the technique will be described and the results of its operation on a specific hyperspectral image presented.