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中文摘要：
传统的模糊C均值聚类算法（FCM）在图像分割中未考虑各个点的灰度特征及其邻域像素的关联程度，导致其对于噪声十分敏感．而各种改进算法虽然较好地克服了图像噪声的影响，但由于使用均值滤波等方法导致分割图像边缘模糊．为此，提出一种基于各向异性权重的FCM图像分割方法，通过引入新的邻域窗口权重的计算方法，使得中心点邻域内各点具有各向异性的权重；并使用基于灰度级的快速算法，提出了各向异性权重的模糊C均值聚类算法．实验结果表明，文中方法具有较强的抗噪性，对于噪声具有良好的稳定性，分割精度较高．
英文摘要：
Since it does not take into account the image characteristics as well as the correlation of neighbor pixels, the standard fuzzy C-means （FCM） is very sensitive to noise. Although some improved methods blurred due to the we propose each pixel algorithm property, a new in the do possess the anti-noise property, their resultant edges of segmentation may be use of low-pass filters, such as the averaged filter. To overcome these drawbacks, FCM based image segmentation method where an anisotropic weight is assigned to neighborhood. In addition, a fast anisotropic weighted fuzzy C-means clustering is also proposed. The experimental results show that our method has the stronger anti-noise better robustness to various noises and higher segmentation accuracy.
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