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Clustering is a predominant technique used in image segmentation due to its simple, easy and efficient approach. It is very important for the analysis, extraction and interpretation of images; which makes it used in multiple applications and in various fields. In this article, we propose a different image segmentation technique based on the cooperation between an optimization algorithm which is the Cuckoo Search Algorithm (CSA) and a clustering technique which is the Fuzzy C-means (FCM). The clustering method we propose goes through two major steps. In the first step, CSA explores the entire search space of the specified data to find the optimal clustering centers. Subsequently, these centers are evaluated using a new objective function. The result of the first step is used to initialize the FCM algorithm in the second step. The efficiency of the suggested method is measured on several images selected from the BSD300 database and we compare it with other algorithms such as FCM optimized by genetic algorithms (FCM-GA) and FCM optimized by particle swarm optimization (FCM-PSO). The experimental results on the different algorithms used in this paper show that the proposed method improves the segmentation results, based on the analysis of the best values of fitness, MSE, PSNR, CC, RI, GCE, BDE and VOI.

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