Image histogram thresholding based on multiobjective optimization

The thresholding process based on the optimization of one criterion only does not work well for a lot of images. In many cases, even when equipped with the optimal value of the threshold of its single criterion, the thresholding program does not produce a satisfactory result. In this paper, we propose to use the multiobjective optimization approach to find the optimal thresholds of three criteria: the within-class criterion, the entropy and the overall probability of error criterion. In addition we develop a new variant of simulated annealing adapted to continuous problems to solve the Gaussian curve-fitting problem. Some examples of test images are presented to compare our segmentation method, based on the multiobjective optimization approach, with that of four competing methods: Otsu method, Gaussian curve fitting-based method, valleyemphasis-based method and two-dimensional Tsallis entropy-based method. From the viewpoints of visualization, object size and image contrast, our experimental results show that the thresholding method based on multiobjective optimization performs better than the competing methods. (c) 2007 Elsevier B.V. All rights reserved.