Automatic Fuzzy Segmentation of Images with Differential Evolution

In this paper we propose a novel fuzzy clustering algorithm for automatically grouping the pixels of an image into different homogeneous regions when the number of clusters is not known a-priori. A soft clustering task in the intensity space of an image is formulated as an optimization problem. We use an improved differential evolution (DE) algorithm to automatically determine the number of naturally occurring clusters in the image as well as to refine the cluster centers. We report extensive performance comparisons among the new method, a recently developed genetic-fuzzy clustering technique and the classical fuzzy c-means algorithm over a test suite comprising ordinary gray scale images and remote sensing satellite images. Such comparisons show, in a statistically meaningful way, the superiority of the proposed technique in terms of speed, accuracy and robustness.