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Possibilistic c-means (PCM) algorithm was proposed to overcome the noise sensitivity of fuzzy c-means (FCM). However, the performance of PCM depends heavily on the initialization, and often deteriorates due to the coincident clustering problem. To overcome these problems, we propose a new hybrid clustering algorithm that incorporates an ACO-based clustering into PCM, namely ACOPCM for noisy image segmentation. Our ACOPCM solves the coincident clustering problem using pre-classified pixel information and provides the near optimal initialization of the number of clusters and their centroids. Experimental results demonstrate that our proposed approach achieves higher segmentation accuracy than PCM and hybrid fuzzy clustering approaches.