A parallel implementation of a genetic algorithm for colonic tissue image classification

Analysis of tissue using image processing techniques is essential for dealing with a number of problems in cancer research. The identification of normal and cancerous colonic mucosa is such a problem. In this paper texture analysis techniques are used to measure certain characteristics of normal and cancerous tissue images. A genetic algorithm undertakes the analysis of those results in order to determine the operations useful for the given problem and in the most appropriate operation combination for the purpose of maximising the classification accuracy. The system developed for undertaking those tasks has been implemented on a cluster of Linux workstations using distributed computing techniques. A distributed programming message-passing library, PVM (Parallel Virtual Machine), provides the basis for building this system.