<![CDATA[Fuzzy c-means algorithm (FCM) is one of the most used clustering methods for image segmentation. However, the conventional FCM algorithm presents some limits like its sensitivity to the noise because it does not take into consideration contextual information and its convergence to local minimum since it is based on a gradient descent method. In this paper, we present a new spatial fuzzy clustering algorithm optimized by the Artificial Bee Colony (ABC) algorithm. ABC-SFCM has two major characteristics. First it tackles better noisy image segmentation by making use of the spatial local information into the membership function. Secondly, it improves the global performance by taking advantages of the global search capability of ABC. Experiments with synthetic and real images show that ABC-SFCM is robust to noise compared to other methods.DOI: http://dx.doi.org/10.11591/ij-ai.v1i3.765]]>
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