A new approach to image segmentation using genetic algorithm with mathematical morphology

This paper presents a new approach to image segmentation using genetic algorithm (GA) in conjunction with morphological operations. The proposed method consists to classify each pixel of the image in pixels belonging either to the object or to the background. It starts by a configuration of individuals randomly generated, representing possible segmentation of the image. The solutions are evaluated through an appropriate fitness function which measures the similarity of the individuals with the desired solution and the fittest ones are selected to reproduce in the next generation. Then, the population progresses to a stable representation where all the individual converges to an optimal solution which is the segmented image. We show the use of the morphological operations in the reproduction step of the GA applied on the selected individuals in order to exploit a priori image information. Tested on gray level images, the presented method has yield good results where objects are well extracted from the background.