Image Segmentation with Minimum Cross Entropy

Image segmentation is used to classify the pixels in different regions according to their intensity level. Several segmentation techniques have been proposed, and some of them use complex operators. An interesting method to choose the best thresholds is the Minimum Cross Entropy (MCET); which provides excellent results for bi–level thresholding. Nevertheless, the extension of the segmentation problem into multiple thresholds increases significantly the computational effort required to find optimal threshold values. Each new threshold adds complexity to the formulation of the problem. Evolutionary algorithms use heuristics to optimize criteria over a finite number of iterations. The correct selection of an Evolutionary algorithm to minimize the MCET directly impacts the performance of the method. Current approaches take a large number of iterations to converge and a high rate of MCET function evaluations. This chapter presents the use of evolutionary algorithms for multilevel thresholding using the MCET.