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Fabric defect image segmentation is not only a key stage on real-time visual detection but also a very difficult problem. A novel method for fabric defect image segmentation using improved Pulse Couple Neural Networks (PCNN) is proposed. According to different gray intensity between the field of defects and the field of no defects, PCNN neuron cell is fired to implement segmentation. The iteration index of PCNN is controlled by the minimum cross entropy. And, segmentation evaluation criteria is also presented in this paper. The validity tests on the developed algorithms have been performed with some fabric defect images. Experimental results show that the proposed method can segment common fabric defect quickly and correctly. It is more effective than other methods using performance evaluation.