Akademik Veri Yönetim Sistemi
11th International Conference on Advances in Statistics , Bologna, İtalya, 25 Nisan - 27 Ağustos 2025, ss.35, (Özet Bildiri)
Image segmentation, a significant step in image processing, is defined as dividing an image into meaningful parts or regions according to specific features. Nowadays, it is widely used in medical images, satellite images, pattern recognition, image analysis, and security systems. The fuzzy c-means (FCM) algorithm is often used for image segmentation because of its simplicity and efficiency. However, the FCM algorithm has some drawbacks. Especially in problems with large and complex data sets, the computational cost increases due to the huge search space and its sensitivity to initial values. However, the algorithm is sensitive to noisy data, which limits its effectiveness and leads to unsatisfactory results. In this study, a hybrid metaheuristic algorithm is proposed to deal with the disadvantages of the FCM. The proposed metaheuristic algorithm is based on FCM's objective function. The proposed algorithm is applied to benchmark and medical images used in image processing. The proposed method is compared with well-known metaheuristic algorithms using cluster validity indices to demonstrate the efficiency of the algorithm. The proposed hybrid approach provides better results in image segmentation as it is less sensitive to initial values and noise. Moreover, the obtained results reveal the superiority of the proposed method over the compared methods.