中文摘要：

为了改进模糊C均值聚类（FCM）算法对初始聚类中心敏感、抗噪性能较差、运算量大的问题,提出一种新的基于蚁群和自适应滤波的模糊聚类图像分割方法（ACOAFCM）.首先,该方法利用改进的蚁群算法确定初始聚类中心,作为FCM初始参数,克服FCM算法对初始聚类中心的敏感;其次,采用自适应中值滤波抑制图像噪声干扰,增强算法的鲁棒性;最后,用直方图特征空间优化FCM目标函数,对图像进行分割,减少运算量.实验结果表明,该方法克服了FCM算法对初始聚类中心的依赖,抗噪能力强,收敛速度快,分割精度高.

英文摘要：

As fuzzy C-means clustering （FCM） algorithm is sensitive to the initial clustering centre ,and lacks enough robustness and also has big computational cost, an novel image segmentation algorithm based on ant colony and histogram fuzzy clustering is proposed.Firstly, the algorithm determines the initial clustering centre as the original parameter of FCM using ant colony algorithm, so as to overcome the sensitivity to the initial clustering centre. Secondly, the algorithm restrains the interference of image noise and enhances the robustness of algorithm by adaptive median filter. Finally, the algorithm optimizes the objective function of FCM with characteristic space of histogram in order to reduce calculation.Experimental results indicate that this algorithm overcomes the dependence on the initial clustering centre of FCM, which brings high robustness and segmentation accuracy, and has more faster convergence speed.